MICB425 Portfolio - Microbial Ecological Genomics: From Genomes to Biomes, Spring 2018
Helen, Lin, 30776132
version April 24, 2018
Module 01
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Module 01 portfolio check
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- Evidence worksheet_03
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- Additional Readings
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Data science Friday
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Installation check
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Origins and Earth Systems
Evidence worksheet 01
The template for the first Evidence Worksheet has been included here. The first thing for any assignment should link(s) to any relevant literature (which should be included as full citations in a module references section below).
You can copy-paste in the answers you recorded when working through the evidence worksheet into this portfolio template.
As you include Evidence worksheets and Problem sets in the future, ensure that you delineate Questions/Learning Objectives/etc. by using headers that are 4th level and greater. This will still create header markings when you render (knit) the document, but will exclude these levels from the Table of Contents. That’s a good thing. You don’t’ want to clutter the Table of Contents too much.
Learning objectives
Describe the numerical abundance of microbial life in relation to ecology and biogeochemistry of Earth systems.
General questions
- What were the main questions being asked?
Some of the main questions the author asked were:
What is the number of prokaryotes in different environments on Earth?
What is the total number of carbon, nitrogen and phosphorus on Earth? How can you find these numbers? What strategies can be approached to find these numbers? What are the limitations of these strategies? How do these numbers affect the potential for genetic diversity?
- What were the primary methodological approaches used?
A lot of the methodological approaches were finding cell counts and average cell counts. The authors also integrated data from other studies as well as extrapolated and estimated using those data. One concerning fact was that they used field studies that weren’t published or verified. Specific examples of this included their estimation of Terrestrial subsurface prokaryotes from groundwater data.
- Summarize the main results or findings.
The main findings include: Prokaryotes are mostly found in three habitats - open ocean, soil and oceanic and terrestrial subsurface. Prokaryotes carbon globally is almost double the total the carbon in living organisms. For example, the total carbon of prokaryotes on Earth is approximately 60-100% the total carbon found in plants. They also have the genetic capacity to evolve and because their genes are widely distributed, this serves as a great opportunity for mutational change.
- Do new questions arise from the results?
Some questions that arise from these results include: How can we explore the diversity of prokaryotes and what strategies can we use to find this? Are there better definitions for what defines a species in prokaryotes? How accurate are the estimates in the paper based on studies and extrapolation? How do we confirm these numbers? And why haven’t we revisited these numbers with the new technology we have now?
- Were there any specific challenges or advantages in understanding the paper (e.g. did the authors provide sufficient background information to understand experimental logic, were methods explained adequately, were any specific assumptions made, were conclusions justified based on the evidence, were the figures or tables useful and easy to understand)?
The paper was challenging in the sense that a lot of data was thrown at you initially without much context. The authors only explained the global context later. They also didn’t explain the methods of the studies where they took their data from or questioned the validity of the numbers they were using. Some of the studies were unpublished.
Evidence Worksheet_02 “Life and the Evolution of Earth’s Atmosphere”
Learning objectives:
Comment on the emergence of microbial life and the evolution of Earth systems
Indicate the key events in the evolution of Earth systems at each approximate moment in the time series. If times need to be adjusted or added to the timeline to fully account for the development of Earth systems, please do so.
- 4.6 billion years ago
Moon formed, Zircons, First signs of life in graphite
- 4.2 billion years ago
First proof that life exists?
- 3.8 billion years ago
Rock sediments that proved sea water stabilization
3.75 billion years ago
- 3.5 billion years ago
First existence of life in microfossils
- 3.0 billion years ago
- Photosynthesis
Greenhouse gases keep earth warm
- 2.7 billion years ago
- Emergence of Eukaryotes
Life on Land
- 2.2 billion years ago
- Rock beds show O2 increase due to Eukaryotes and microbes
Cellular cybernetic switch (mitochondria/chloroplasts)
- 2.1 billion years ago
The great oxidation event
- 1.3 billion years ago
Snowball Earth –> great glaciation
- 550,000 years ago
Animal and plant emergence
- 200,000 years ago
Gigantism (ie. Dragonflies emerge due to increased oxygen)
- 4.6 billion years ago
Describe the dominant physical and chemical characteristics of Earth systems at the following waypoints:
- Hadean
- Late heavy bombardements
Life exists starting at this point?
- Archean
Early photosynthesis, sulfur reduction, greenhouse gases
- Precambrian
Glaciation
Proterozoic The great oxidation
Phanerozoic
- Hadean
Evidence Worksheet_03 “The Anthropocene”
Learning objectives:
• Evaluate human impacts on the ecology and biogeochemistry of Earth systems.
Waters, Colin N. et al. #### Specific Questions:
- What were the main questions being asked? How are Quaternary stratigraphic units defined? What are the human drivers of stratigraphic signatures? And what are the linked force multipliers? What are some modifications of the sedimentary processes over the years? Are there any changed geochemical signatures in recend sediments and ice and what are those changes? What are the evidence for the changes in these sediments and the transitions of of the eras?
What were the primary methodological approaches used?
They used anthropogenic markers of functional changes in the Earth through the stratigraphic record. For example they looked at sediments that coincide with global spikes and novel markers like concrete, plastics, global black carbon and plutonium
Summarize the main results or findings.
Human activity is leaving a major signature and effect on Earth.
Holocene epoch is being considered for subdivision into 3 sub-epochs: Lower Holocene, Upper Holocene, Middle Holocene using climatic signatures.
Human forces responsible for anthropogenic signatures are product of 3 linked force multipliers: accelerated technological development, rapid growth of population, and increased consumption of resources – resulting in increased use of land resources
New anthropogenic deposits found in modern wastelands are polymers can be an artifact as well as radiogenic signatures in sediments and ice, carbon cycle evidence, climate change/sea-level change, biotic change areall stratigraphic signatures that make the case for a new epoch.
Do new questions arise from the results?
How are they going to determine where to from what year the new epoch will start? Why do they choose that year? And what are the defining factors?
Were there any specific challenges or advantages in understanding the paper (e.g. did the authors provide sufficient background information to understand experimental logic, were methods explained adequately, were any specific assumptions made, were conclusions justified based on the evidence, were the figures or tables useful and easy to understand)?
They never propose a method to determine the start for the Anthropocene. The figures are also hard to understand.
Problem set 01
Learning objectives:
Describe the numerical abundance of microbial life in relation to the ecology and biogeochemistry of Earth systems.
Specific questions:
- What are the primary prokaryotic habitats on Earth and how do they vary with respect to their capacity to support life? Provide a breakdown of total cell abundance for each primary habitat from the tables provided in the text.
Aquatic Environments (includes seawater, lakes, deep oceanic water any sort of enivronment with large bodies of water) Fresh Water cell abundance is around 10x10^5 cells/mL Continental Shelf is 5x10^5 Cells/mL same as open ocean upper 200m water Ocean water below 200m is 0.5x10^5 cells/mL, and Ocean Sediment is 4600x10^5 cells/mL Total 1.181 x 10^29 Cells
Subsurface (defined as terrestrial habitats below 8m and marine sediments below 10cm and prokaryote population is quite large, it’s mostly inorganic and only a subset of prokaryotes can survive) Deep oceans have more cell density compared to the continental shelf and coastal plains, and the deeper you go the less cells you have per cm^3 starting from 220 x 10^6 cells/cm^3 to 0.34 cells/cm^3 from 0.1m down to 3000 m down. Total 2.556 x 10^29 Cells
Soil (Major reservoir of organic carbon on earth and prokaryotes are essential to decomposition, also includes grasslands and cultivated soils) Depending on what kind of soil (forest is around 0.3 to 0.6 x 10^27 cells/g of soil, other land including grass and cultivated, alpine, savanna woodland are upwards of 20.8 to 63 x 10^27 cells/g).
- What is the estimated prokaryotic cell abundance in the upper 200 m of the ocean and what fraction of this biomass is represented by marine cyanobacterium including Prochlorococcus? What is the significance of this ratio with respect to carbon cycling in the ocean and the atmospheric composition of the Earth?
The upper 200 m of ocean the cellular density is about 5x10^5 cells/mL and a portion of these cells are Prochlorococcus with an average cell density of 4x10^4 cells/mL (so it accounts for 8%).
3.6 x 10^28 cells total found
This provides high turnover of prokaryotes and produces a large amount of carbon, four times their carbon content) and there is a net productivity of 85% at the upper 200 m of the ocean. So it also contributes significantly to the atmospheric composition of the Earth.
- What is the difference between an autotroph, heterotroph, and a lithotroph based on information provided in the text?
Autotroph - sustains itself using light energy since they’re only found on surface of the ocean, “self-nourishing” and fix inorganic carbon (CO2) which accumulates biomass
Heterotroph - consumes organic carbon to sustain itself
Lithotroph - consumes inorganic substrates to sustain itself.
- Based on information provided in the text and your knowledge of geography what is the deepest habitat capable of supporting prokaryotic life? What is the primary limiting factor at this depth?
Deepest is subsurface sediments at a depth of 3 to 4 km The limiting factor at this depth is temperature since the average temperature reaches 125 degrees C which is the upper limit for supporting prokaryotic life.
- Based on information provided in the text your knowledge of geography what is the highest habitat capable of supporting prokaryotic life? What is the primary limiting factor at this height?
The highest habitat capable of supporting prokaryotic life is air, they’ve been detected up to 57-77 km Primary limiting factor is that there aren’t many nutrients or organic matter for that matter.
- Based on estimates of prokaryotic habitat limitation, what is the vertical distance of the Earth’s biosphere measured in km?
81 km because of 77 km above land plus 4 km below land
If we only count Mount Everest as the top plus Mariana’s Trench (10.9 km) plus 4.5km below that then the total is 24km total.
- How was annual cellular production of prokaryotes described in Table 7 column four determined? (Provide an example of the calculation)
365 days divided by turn over time (in days) multiply that by population size
gives you cells/year
(Population x (turnover/yr) = cells/yr [(3.6 x 10^28 cells)*365 days] / 16 turnovers = 8.2 x 10^29 cells/year.
- What is the relationship between carbon content, carbon assimilation efficiency and turnover rates in the upper 200m of the ocean? Why does this vary with depth in the ocean and between terrestrial and marine habitats?
Carbon Much higher in top 200 m of ocean because of photosynthesis at the top, bottom are feeding off of material the top prokaryotes make Assuming carbon efficiency is 20%, and 20 fg of carbon/cell —even be around 20 x 10^-30 pg/cell (3.6 x 10^28 cells) * (20 x 10^-30 pg of carbon/cell) = 0.72 Pg of Carbon in marine heteros 4 (turnover) x 0.72 Pg = 2.88 Pg/yr
turnover rate is also higher in the 200 m above the ocean
It takes a lot longer (turnover time) in terrestrial habitats, whereas there’s an 85% productivity in oceanic prokaryotes 51Pg of carbon/year with 85% consumed = 43 Pg C (43 Pg c/Yr) / (2.88 Pg/year) = 14.4 or 1 turnover every 24.5 days.
- How were the frequency numbers for four simultaneous mutations in shared genes determined for marine heterotrophs and marine autotrophs given an average mutation rate of 4 x 10-7 per DNA replication? (Provide an example of the calculation with units. Hint: cell and generation cancel out)
365 days/16 days for turnover = 22.8 turnover/yr (3.6 x 10^28 cells) x 22.8 turnover/yr = 8.2 x 10^29 cells/yr (4 x 10-7)4 mutations/generation = 2.56 x 10^-26 mut/gen (power to four because we want 4 mutations to happen simultaneously (8.2 x 10^29) cells/yr x (2.56 x 10^-26) mut/year = 21,012 mut/yr (8.7 x 10^3 hr/yr)/ (21012 mut/yr) = 0.4 mut/hr
- Given the large population size and high mutation rate of prokaryotic cells, what are the implications with respect to genetic diversity and adaptive potential? Are point mutations the only way in which microbial genomes diversify and adapt?
The more mutational potential and the higher the mutation rate of prokaryotic cells, the greater the genetic diversity possible for prokaryotes as well as a more advantageous adaptive potential
Other mutations are possible such as plasmid transfer, recombination, transformation, transduction..etc.
- What relationships can be inferred between prokaryotic abundance, diversity, and metabolic potential based on the information provided in the text?
Abundance and rapid replication lead to high mutation rates which cause an increase in diversity of the prokaryotic population. The more diversity there is, the more potential to adapt to environmental stresses and also an increase in metabolic potential.
Problem set_02 “Microbial Engines”
Learning objectives:
Discuss the role of microbial diversity and formation of coupled metabolism in driving global biogeochemical cycles.
Specific Questions:
** What are the primary geophysical and biogeochemical processes that create and sustain conditions for life on Earth? How do abiotic versus biotic processes vary with respect to matter and energy transformation and how are they interconnected?**
Geophysical: Techtonic plates and atmospheric photochemical mainly
Biogeochemical: Redox reactions of HCONSP
– Biological oxidation of earth is driven by photosynthesis
– Metabolic map of the six major elements contribute
Abiotic geochemical reactions based on acid/base chemistry (transfer of protons without electrons)
** Why is Earth’s redox state considered an emergent property?**
Reaction alter the surface redox state of the planet
– Feedbacks between evolution of microbial metabolic and geochemical processes create average redox conditions of oceans and atmosphere
** How do reversible electron transfer reactions give rise to element and nutrient cycles at different ecological scales? What strategies do microbes use to overcome thermodynamic barriers to reversible electron flow?**
Low hydrogen tension then reverse process become thermodynamically favourable. Synergistic cooperation of multispecies assemblages (sulfate reducers consume hydrogen)
Redox reaction drive the Earth’s nitrogen cycle. Citric acid cycle oxidizes into CO2, and also assimilates CO2 into organic matter
** Using information provided in the text, describe how the nitrogen cycle partitions between different redox “niches” and microbial groups. Is there a relationship between the nitrogen cycle and climate change? **
Some microbes are predicted to exist soley on the basis of thermodynamics. Oxygen of N2 to NO3 to NO2 is not thermodynamically fabourable. No known photosynthetic organisms can facilitate oxidation of ammonia which is not available for photosynthesis.
** What is the relationship between microbial diversity and metabolic diversity and how does this relate to the discovery of new protein families from microbial community genomes? **
Microbial diversity comes in the diversity in the gene sets of microbes. Different species of microbes have different genomes. The explosion of genomic information has led to a limitless evolutionary diversity in nature. And within those genesets there are important genes that encode for important components of various biogeochemical cycles (metabolic diversity). Several approaches have been used to evaluate the depth of protein families through microbial community genome sequencing (metagenomics).
** On what basis do the authors consider microbes the guardians of metabolism? **
Dispersal of core planetary gene set by vertical or horizontal gene transfer has allowed a wide variety of organisms to become guardians of metabolism. Environmental selection allows the evolution of boutique genes to protect the metabolic pathway if one set of microbes goes extinct.
Module 01 Writing Assignment
Prompt
“Microbial life can easily live without us; we, however, cannot survive without the global catalysis and environmental transformations it provides.” Do you agree or disagree with this statement? Answer the question using specific reference to your reading, discussions and content from evidence worksheets and problem sets.
Opening
From the beginning of the Earth’s start, microbes have developed as well as grow the various biogeochemical cycles of the environment. As humans continue to contribute to the global warming phenomenon and release hazardous chemicals into various habitats, microbes have been there to react and adapt to the changing circumstances. If we slowly destroy the habitat at a pace that microbes cannot adapt to, then we will not be able to ensure our own survival. Microbes have been vital to shaping the earth’s biogeochemical cycles throughout the centuries. Humans cannot completely cover the extent of microbial actions to ameliorate the environment. We will not be able to survive without microbial life since we cannot replicate microbial processes that occur organically. We’ve relied on the global catalysis and environmental transformations that microbes provide for so long. In this essay, I will explore the theme that humans cannot survive without the help of microbes and their various processes.
Microbes shape the cycles of the Earth
There is no doubt that microbes have shaped the Earth since the early Archaen period, developing the cycles we know today like the nitrogen cycle and the oxygen cycle. The production of oxygen, something that all humans need and cannot live without, was mainly done by microbes. Marine photosynthesis by single cell organisms is a net source of O2 on Earth [1]. This oxygen is created by the reaction: CO2 + H2O CH2O + O2 where CH2O is shorthand for complex organic matter [1]. The initial rise of atmospheric O2 was caused by a primitive cyanobacteria at least 2.3 billion years ago even though the process of oxygenic photosynthesis is extremely complicated [1]. Even though humans have comprehensive and multi-faceted technology, it is almost impossible for us to replicate such a complex system from scratch.
Another important cycle that microbes contribute to is the nitrogen cycle. The biosphere needs incorporation of nitrogen into biological molecules which is why prokaryotes are truly vital for their ability to reduce nitrogen gas (N2) to ammonium [2]. It is suggested that anoxygenic phototrophs contributed to the flux of nutrients and carbon through the biosphere [2]. And due to this, Nitrogen limitation would cause selective pressure anoxygenic phototrophs to fix N2 [2]. This happened early on in biological evolution in anaerobic photoautotrophs under the domain Bacteria [2]. Without this important process, many current organisms and biological processes would not be possible – including human life.
Other important pathways include the major biogeochemical fluxes, or in other words the biological fluxes of H, C, N, O, S which are largely driven by microbes [3]. Sulphate and Sulphur respiration as well as iron respiration cycles are mediated by many microbes, often in anaerobic habitats [3]. Another major important biogeochemical cycle that microbes facilitate is the nitrogen cycle. Various oxidation and reduction reactions that drive the nitrogen cycle are catalyzed by diverse multispecies microbial interactions [3]. Oxidation of ammonia to NO2- requires a specific group of Bacteria and Archaea [3]. Subsequent oxidization to NO3 is facilitated by nitrifying bacteria [3]. Nitrifiers use small differences in redox potential in oxidation reactions to reduce carbon dioxide to organic matter, and a different set of microbes use NO2 and NO3 as electron acceptors in anaerobic oxidation of organic matter [3]. All these redox pathways are driven by different microbes, and all contribute to influencing the availability of organic matter on Earth. As we can see, the prevalence of so many central biogeochemical cycles are due to the many microbes that support the chemical reactions. Humans are dependent on these pathways that maintain the Earth’s habitable environment, but we cannot provide support like the microbes do.
Humans cannot replicate microbial actions
Despite the many planetary boundary and intervention techniques humans try to take in order to ameliorate the damage that has already been caused, we cannot adapt and stretch our technology to cover the entirety of the Earth’s atmosphere as microbes do. According to Whitman et al., the number of prokaryotes in the open ocean is around 1.2 x 1029 cells [4]. Of course, these numbers may be inaccurate, but imagine this large number of cells that contribute to oxidative photosynthesis described above, shaping the oxygenic atmosphere that many organisms require. The cellular production rate for prokaryotes is estimated at 1.7 x 1030 cells/year, with the highest rate at the open ocean [4]. This enormous contribution of microbes to the atmosphere seems impossible to replicate or even adapt at the microbial pace.
The techniques and methods that humans have in place to restore the planet’s environment are meek at best. Johan Rockstrom speaks on the framework of planetary boundaries that humans should operate within to respect the Earth’s biophysical processes [5]. These thresholds are essentially a critical value for control variables, like CO2 concentration, which we have to maintain in order to contain an already teetering global environment from completely breaking down [5]. The Earth’s biodiversity is so complex that setting such an arbitrary boundary is difficult [5]. And because of microbes and various species that support the ecosystem, we cannot know for sure how much biodiversity can be lost before the Earth’s resilience erodes [5]. The entire argument that humans can restore the ecology of the Earth rests on our ability to understand the “scale of human action in relation to the capacity of the Earth to sustain it” [5]. Without complete understanding and capacity to change what’s broken, humans will never have the opportunity to reverse global climate change.
Humans depend on microbial processes
Humans are directly dependent on many of the elements of the biogeochemical cycles described above. We will not survive without the existence of microbes. The reason for increasing large scale environmental pollution is due to modern agriculture [5]. Because we require the presence of nitrogen as fertilizers in our crops, we have slowly but surely poisoned the soil and seas with excessive nutrients. The manufacturing of fertilizer for food products convert up to 120 million tonnes of N2 from the atmosphere [5]. Phosphorus, a result of geological processes, is also mined from rock for humans to use as fertilizer or even toothpaste [5]. It goes without saying that humans and countless other organisms require oxygen in the air we breathe in order for basic survival. Man is at the top of the pyramid of life, but each successive layer depends on the ones below it for food and biological services [6].
Many of these mined or converted chemicals are disposed in the environment causing a buildup of even more environmental damage. Marine ecosystems take the brunt of these anthropogenic distortions of nitrogen and phosphorus cycles [5]. Reactive oxygen pollutes waterways, coastal zones and land systems, thereby contributing to gases in the atmosphere [5]. 9.5 million tonnes of phosphorus also end up in oceans, eight times its normal background rate of influx [5]. We use the resources that microbes provide, but we do not take care to restore or maintain the pathways in which we take chemicals from.
Closing
Microbes are essential to the survival of humans and other organisms, but humans are not essential for the survival of all life on Earth. The various biogeochemical cycles like oxygen and nitrogen pathways are evolved and shaped to what they are today by microbes. And because there are massive quantities of microbes in the ecosystem, there is no possibility that human advancement in technology could ever replicate the actions or take the place of microbes. We cannot even guarantee our own survival without the presence of microbes since we are largely reliant on microbial pathways for survival. Humans take nutrients from the environment and disrupt the processes we take them from. Should we adhere to the boundaries we have set ourselves to follow in order to try to slow down the Earth’s eroding ecosystem? Or should we try to understand the various microbial pathways that contribute to this ecosystem even better to come up with a solution? Will we ever fully understand the complexities of the Earth’s ecosystem to change anything significantly? These questions can only be answered by testing our perseverance and commitment to find a long-lasting solution to climate change.
Module 01 references
Utilize this space to include a bibliography of any literature you want associated with this module. We recommend keeping this as the final header under each module.
Kasting, James F., and Siefert, Janet L. 2003
Nisbet, E.G., Sleep, N.H. 2003
Module 01 Writing Assignment References
Kasting, James F., and Siefert, Janet L. “Life and Evolution of Earth’s Atmosphere.” Science 296 (2002): 1066-1067. Print.
Canfield, Daniel E. “The Evolution and Future of Earth’s Nitrogen Cycle.” Science 330, 192 (2010): 192-196. Print.
Falkowski, Paul G. “The Microbial Engines That Drive Earth’s Biogeochemical Cycles.” Science 330, 1034 (200): 1034-1039. Print.
Whitman, William B., et al. “Prokaryote: The unseen majority.” National Academy of Sciences, vol. 95, no. 12, 09 Jun. 1998, pp. 6578-6583.
Rockstrom, Johan. et al “A safe operating space for humanity.” Nature 461|24 (2009): 472-475. Print.
Leopold, Aldo, 1886-1948. A Sand County Almanac, and Sketches Here and There. New York :Oxford University Press, 1949. Print.
Module 02
Evidence Worksheet_04 “Bacterial Rhodopsin Gene Expression”
Learning objectives:
*Discuss the relationship between microbial community structure and metabolic diversity
*Evaluate common methods for studying the diversity of microbial communities
*Recognize basic design elements in metagenomic workflows
General Questions:
What were the main questions being asked?
What are the specific functions and physiological roles of diverse marine proteorhodopsins (PRs)? What are the characteristics of PR photosystem structure and function? Describe the intact PR-based photosystems and its function in E. coli.
What were the primary methodological approaches used?
Screening large-insert DNA libraries derived from marine picoplankton for in Vivo PR photosystem expression
Genomic Analysis of PR photosystem-expressing clones
Genetic and Phenotypic Analysis of PR Photosystems
In Vitro Transposition and Translocation
Carotenoid Extraction
HPLC Analysis
Proton Pumping Experiments
Summarize the main results or findings.
PR photosystem recombinants characterized could be detected visually by pigment production and exhibited light-dependent proton translocation and subsequent photophosphorylation
Only six genes are required to enable light-activated proton translocation fully in a heterologous host - found in a wide variety of different marine bacterial taxa, both necessary and sufficient for complete synthesis and assembly of a fully functional PR photoprotein in E. coli.
PR photosystem-catalyzed photophosphorylation is consistent with proposed role for PR in marine microbial photoheterotrophy
Do new questions arise from the results?
If photophosphorylation is possible in microbial organisms through lateral transfer, then what else is possibly transfered that we don’t know about?
Were there any specific challenges or advantages in understanding the paper (e.g. did the authors provide sufficient background information to understand experimental logic, were methods explained adequately, were any specific assumptions made, were conclusions justified based on the evidence, were the figures or tables useful and easy to understand)?
Probably possible for the authors to compact the information to be more to the point. A lot of figures are not what I’m used to seeing - like proton pumping assays I have never encountered. It may be good to explain the graphs a bit more.
Problem set_03 “Metagenomics: Genomic Analysis of Microbial Communities”
Learning objectives:
Specific emphasis should be placed on the process used to find the answer. Be as comprehensive as possible e.g. provide URLs for web sources, literature citations, etc.
(Reminders for how to format links, etc in RMarkdown are in the RMarkdown Cheat Sheets)
Specific Questions:
- How many prokaryotic divisions have been described and how many have no cultured representatives (microbial dark matter)?
2016: ~89 bacteria phyla, and 20 archael phyla via small 16S rRNA databases, but could be up to 1500 bacteria phyla. There are microbes that live in the “shadow biosphere”.
2003: ~26 of 52 major bacterial phyla have been cultivated, and there are probably more now. But there are still many undiscovered divisions.
- How many metagenome sequencing projects are currently available in the public domain and what types of environments are they sourced from?
There are many metagenomic sequencing projects available in the public domain, up to thousands. For example 110,217 are found on the EBI database, and the ones that are there are always changing. They’re also sourced from all types of enviornments (sediments, soil, gut aquatic…etc), especially those where it’s hard to culture communities in lab settings.
- What types of on-line resources are available for warehousing and/or analyzing environmental sequence information (provide names, URLS and applications)?
For MLST, there is an online resource for identifying OTU identification. https://www.researchgate.net/publication/275946089_Metagenomics_Tools_and_Insights_for_Analyzing_Next-Generation_Sequencing_Data_Derived_from_Biodiversity_Studies The different categories include:
Shotgun Metagenomics: - Assembly - for example EULER, Velvet
Binning - ex. TETRA, MetaCluster
Annotation - ex. FASTX-Toolkit, mOTU-LG
Analysis pipeline - ex. CAMERA
Marker gene metagenomics:
Standalone software - ex. OTUbase
Analysis pipelines - ex. SILVA, CLOTU
Denoising - ex. JATAC
Databases - ex. RDP (Ribosomal Database Project)
What is the difference between phylogenetic and functional gene anchors and how can they be used in metagenome analysis?
Phylogenetic gene anchors are defined by things like vertical gene transfer, carrying phylogenetic information allowing tree reconstruction, single-copy, taxonomic. Functionall gene anchors have more horizontal gene transfer, and can identify specific biogeodom functions associated with measurable effects. But they are not as useful for phylogeny.
- What is metagenomic sequence binning? What types of algorithmic approaches are used to produce sequence bins? What are some risks and opportunities associated with using sequence bins for metabolic reconstruction of uncultivated microorganisms?
MEtagenomic binning is the process of grouping sequences that come from a single genome. The types of algorithms include (1) Aligning sequences to the database and (2) Grouping to each other based on DNA characteristics: GC content, codon usage.
Some risks of binning include: incomplete coverage of genome sequence, contamination from different phylogeny.
- Is there an alternative to metagenomic shotgun sequencing that can be used to access the metabolic potential of uncultivated microorganisms? What are some risks and opportunities associated with this alternative?
Some alternatives include: functional screens (biochemical), 3rd gen sequencing (nanopore), single cell sequencing, FISH probe…etc.
Module 02 references
Module 03
Evidence Worksheet_05 “Extensive mosaic structure”
Learning objectives:
Evaluate the concept of microbial species based on environmental surveys and cultivation studies.
Explain the relationship between microdiversity, genomic diversity and metabolic potential
Comment on the forces mediating divergence and cohesion in natural microbial communities
General Questions:
What were the main questions being asked?
What are the genetic differences and pathological differences between CFT073, an enterohemmorhagic E. coli and various other strains of E. coli
What were the primary methodological approaches used?
Isolated, sequenced, BLAST for proteins
MAGPIE annotations for ORFs
Sequence by SEQMANII
Primer walking
Codon usage tested between backbone and island gene
Summarize the main results or findings.
No virulence plasmids
5 Circular genome is 5,231,428 bp
39.2% combined (nonredundant) set of proteins common to all three strains
Major differences exist between large pathogenicity islands between CFT073 and J96 and 536 strains
Same genes by vertical gene transfer
Different islands in the backbone acquired by horizontal gene transfer
Location of islands are often similar within the genome however they code for different things
Islands tend to have adaptive traits which lead to fitness of the species and selection of the environment. This leads to the specific ecotype and pathogenesis of the species.
Do new questions arise from the results?
Analyze all islands and how do all these islands affect the pathogen Deletion of these islands affect their survival in the habitat?
Presence of black holes (driver for losing genes) have gone?
How long does it take to provide these results now? And which methods would be best used?
Were there any specific challenges or advantages in understanding the paper (e.g. did the authors provide sufficient background information to understand experimental logic, were methods explained adequately, were any specific assumptions made, were conclusions justified based on the evidence, were the figures or tables useful and easy to understand)?
It would be nice to have table for genome content for the other 2 species
Explore other strains of E. coli to be more confident about the results
The conclusions and the content of this paper was perhaps very advanced for its time. The results were clear and insightful. Good for being on the cusp
Part 2: Learning Objectives
Comment on the creative tension between gene loss, duplication and acquisition as it relates to microbial genome evolution
Identify common molecular signatures used to infer genomic identity and cohesion
Differentiate between mobile elements and different modes of gene transfer
Based on your reading and discussion notes, explain the meaning and content of the following figure derived from the comparative genomic analysis of three E. coli genomes by Welch et al. Remember that CFT073 is a uropathogenic strain and that EDL933 is an enterohemorrhagic strain. (1) Explain how this study relates to your understanding of ecotype diversity. (2) Provide a definition of ecotype in the context of the human body. (3)Explain why certain subsets of genes in CFT073 provide adaptive traits under your ecological model and speculate on their mode of vertical descent or gene transfer.
- Depending on the strain of E. coli, they have different habitats (either in the GI tract, or in the urinary tract) and their own subset of distinct genes that provide an advantage for their survival in that habitat. This is beneficial to their ecotype diversity.
- The ecotype is defined as specific strains of organisms that inhabit specific organs like E. coli in the urinal tract.
- Horizontal gene transfer is most likely.
Problem set_04 “Fine-scale phylogenetic architecture”
Learning objectives:
- Gain experience estimating diversity within a hypothetical microbial community
Part 1: Description and enumeration
Obtain a collection of “microbial” cells from “seawater”. The cells were concentrated from different depth intervals by a marine microbiologist travelling along the Line-P transect in the northeast subarctic Pacific Ocean off the coast of Vancouver Island British Columbia.
Sort out and identify different microbial “species” based on shared properties or traits. Record your data in this Rmarkdown using the example data as a guide.
Once you have defined your binning criteria, separate the cells using the sampling bags provided. These operational taxonomic units (OTUs) will be considered separate “species”. This problem set is based on content available at What is Biodiversity.
For example, load in the packages you will use.
#To make tables
library(kableExtra)
library(knitr)
#To manipulate and plot data
library(tidyverse)Then load in the data. You should use a similar format to record your community data.
example_data1 = data.frame(
number = c(1,2,3),
name = c("lion", "tiger", "bear"),
characteristics = c("brown cat", "striped cat", "not a cat"),
occurences = c(2, 4, 1)
)Finally, use these data to create a table.
example_data1 %>%
kable("html") %>%
kable_styling(bootstrap_options = "striped", font_size = 10, full_width = F)| number | name | characteristics | occurences |
|---|---|---|---|
| 1 | lion | brown cat | 2 |
| 2 | tiger | striped cat | 4 |
| 3 | bear | not a cat | 1 |
For your sample:
#This is the population
library(kableExtra)
library(knitr)
library(tidyverse)
diversity_candy = data.frame(
number = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14),
name = c("Skittles", "Vines", "Mikes & Ikes", "Bricks", "Gummy Bears", "M&M", "Sour Bears", "Sour Fruit", "Hershey Kisses", "Jujubes", "Sour Bottle", "Sour Swirl", "Wine Candy", "Sour Hexa"),
characteristics = c("chewy sour candy", "red string candy", "rod shaped fruit candy", "brick shaped candy", "gummy bear candy", "circular chocolate candy", "sour bear candy", "sour fruit shaped candy", "cone shaped chocolate candy", "hard gummy candy", "sour bottle-shaped candy", "sour swirl candy", "wine candy", "sour 6-legged candy"),
occurences = c(187, 14, 174, 18, 101, 241, 3, 2, 16, 24, 3, 3, 9, 6))
kable(diversity_candy, "html") %>%
kable_styling(bootstrap_options = "striped", font_size = 10, full_width = F)| number | name | characteristics | occurences |
|---|---|---|---|
| 1 | Skittles | chewy sour candy | 187 |
| 2 | Vines | red string candy | 14 |
| 3 | Mikes & Ikes | rod shaped fruit candy | 174 |
| 4 | Bricks | brick shaped candy | 18 |
| 5 | Gummy Bears | gummy bear candy | 101 |
| 6 | M&M | circular chocolate candy | 241 |
| 7 | Sour Bears | sour bear candy | 3 |
| 8 | Sour Fruit | sour fruit shaped candy | 2 |
| 9 | Hershey Kisses | cone shaped chocolate candy | 16 |
| 10 | Jujubes | hard gummy candy | 24 |
| 11 | Sour Bottle | sour bottle-shaped candy | 3 |
| 12 | Sour Swirl | sour swirl candy | 3 |
| 13 | Wine Candy | wine candy | 9 |
| 14 | Sour Hexa | sour 6-legged candy | 6 |
- Construct a table listing each species, its distinguishing characteristics, the name you have given it, and the number of occurrences of the species in the collection.
diversity_candy_sample = data.frame(
number = c(1,2,3,4,5,6,7,8,9,10,11),
name = c("Skittles", "Vines", "Mikes & Ikes", "Bricks", "Gummy Bears", "M&M", "Hershey Kisses", "Jujubes", "Sour Swirl", "Wine Candy", "Sour Hexa"),
characteristics = c("chewy sour candy", "red string candy", "rod shaped fruit candy", "brick shaped candy", "gummy bear candy", "circular chocolate candy", "cone shaped chocolate candy", "hard gummy candy", "sour swirl candy", "wine candy", "sour 6-legged candy"),
occurences = c(49, 8, 40, 3, 23, 44, 2, 3, 1, 1, 2))
kable(diversity_candy_sample, "html") %>%
kable_styling(bootstrap_options = "striped", font_size = 10, full_width = F)| number | name | characteristics | occurences |
|---|---|---|---|
| 1 | Skittles | chewy sour candy | 49 |
| 2 | Vines | red string candy | 8 |
| 3 | Mikes & Ikes | rod shaped fruit candy | 40 |
| 4 | Bricks | brick shaped candy | 3 |
| 5 | Gummy Bears | gummy bear candy | 23 |
| 6 | M&M | circular chocolate candy | 44 |
| 7 | Hershey Kisses | cone shaped chocolate candy | 2 |
| 8 | Jujubes | hard gummy candy | 3 |
| 9 | Sour Swirl | sour swirl candy | 1 |
| 10 | Wine Candy | wine candy | 1 |
| 11 | Sour Hexa | sour 6-legged candy | 2 |
- Ask yourself if your collection of microbial cells from seawater represents the actual diversity of microorganisms inhabiting waters along the Line-P transect. Were the majority of different species sampled or were many missed?
There are most likely many missing because there are far more than 40 different species of microorganisms. The scale cannot be compared with the context of candy.
Part 2: Collector’s curve
To help answer the questions raised in Part 1, you will conduct a simple but informative analysis that is a standard practice in biodiversity surveys. This analysis involves constructing a collector’s curve that plots the cumulative number of species observed along the y-axis and the cumulative number of individuals classified along the x-axis. This curve is an increasing function with a slope that will decrease as more individuals are classified and as fewer species remain to be identified. If sampling stops while the curve is still rapidly increasing then this indicates that sampling is incomplete and many species remain undetected. Alternatively, if the slope of the curve reaches zero (flattens out), sampling is likely more than adequate.
To construct the curve for your samples, choose a cell within the collection at random. This will be your first data point, such that X = 1 and Y = 1. Next, move consistently in any direction to a new cell and record whether it is different from the first. In this step X = 2, but Y may remain 1 or change to 2 if the individual represents a new species. Repeat this process until you have proceeded through all cells in your collection.
For example, we load in these data.
example_data2 = data.frame(
x = c(1,2,3,4,5,6,7,8,9,10),
y = c(1,2,3,4,4,5,5,5,6,6)
)My own data:
candy_sample_collector_ = data.frame(x = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175),
y = c(1,2,3,3,3,4,4,5,6,7,8,8,9,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11)
)And then create a plot. We will use a scatterplot (geom_point) to plot the raw data and then add a smoother to see the overall trend of the data.
ggplot(example_data2, aes(x=x, y=y)) +
geom_point() +
geom_smooth() +
labs(x="Cumulative number of individuals classified", y="Cumulative number of species observed")## `geom_smooth()` using method = 'loess'
My plot:
ggplot(candy_sample_collector_, aes(x=x, y=y)) +
geom_point() +
geom_smooth(method="loess") +
labs(x="Cumulative number of individuals classified", y="Cumulative number of species observed")
For your sample:
- Create a collector’s curve.
- Does the curve flatten out? If so, after how many individual cells have been collected?
- What can you conclude from the shape of your collector’s curve as to your depth of sampling?
Part 3: Diversity estimates (alpha diversity)
Using the table from Part 1, calculate species diversity using the following indices or metrics.
Diversity: Simpson Reciprocal Index
\(\frac{1}{D}\) where \(D = \sum p_i^2\)
\(p_i\) = the fractional abundance of the \(i^{th}\) species
For example, using the example data 1 with 3 species with 2, 4, and 1 individuals each, D =
species1 = 2/(2+4+1)
species2 = 4/(2+4+1)
species3 = 1/(2+4+1)
1 / (species1^2 + species2^2 + species3^2)## [1] 2.333333The higher the value is, the greater the diversity. The maximum value is the number of species in the sample, which occurs when all species contain an equal number of individuals. Because the index reflects the number of species present (richness) and the relative proportions of each species with a community (evenness), this metric is a diveristy metric. Consider that a community can have the same number of species (equal richness) but manifest a skewed distribution in the proportion of each species (unequal evenness), which would result in different diveristy values.
- What is the Simpson Reciprocal Index for your sample?
species1 = 49/175
species2 = 8/175
species3 = 40/175
species4 = 3/175
species5 = 23/175
species6 = 44/175
species7 = 2/175
species8 = 3/175
species9 = 1/175
species10 = 1/175
species11 = 2/175
1/(species1^2 + species2^2 + species3^2 + species4^2 + species5^2 + species6^2 + species7^2 + species8^2 + species9^2 + species10^2 + species11^2)## [1] 4.669869## Ans = 4.66986886- What is the Simpson Reciprocal Index for your original total community?
species1 = 187/801
species2 = 14/801
species3 = 174/801
species4 = 18/801
species5 = 101/801
species6 = 241/801
species7 = 3/801
species8 = 2/801
species9 = 16/801
species10 = 24/801
species11 = 3/801
species12 = 3/801
species13 = 9/801
species14 = 6/801
1/(species1^2 + species2^2 + species3^2 + species4^2 + species5^2 + species6^2 + species7^2 + species8^2 + species9^2 + species10^2 + species11^2 + species12^2 + species13^2 + species14^2)## [1] 4.75165## Answer is 4.75164967Richness: Chao1 richness estimator
Another way to calculate diversity is to estimate the number of species that are present in a sample based on the empirical data to give an upper boundary of the richness of a sample. Here, we use the Chao1 richness estimator.
\(S_{chao1} = S_{obs} + \frac{a^2}{2b})\)
\(S_{obs}\) = total number of species observed a = species observed once b = species observed twice or more
So for our previous example community of 3 species with 2, 4, and 1 individuals each, \(S_{chao1}\) =
3 + 1^2/(2*2)## [1] 3.25- What is the chao1 estimate for your sample?
11 + 6^2/(2*5)## [1] 14.6##Ans = 14.6Part 4: Alpha-diversity functions in R
We’ve been doing the above calculations by hand, which is a very good exercise to aid in understanding the math behind these estimates. Not surprisingly, these same calculations can be done with R functions. Since we just have a species table, we will use the vegan package. You will need to install this package if you have not done so previously.
library(vegan)First, we must remove the unnecesary data columns and transpose the data so that vegan reads it as a species table with species as columns and rows as samples (of which you only have 1).
example_data1_diversity =
example_data1 %>%
select(name, occurences) %>%
spread(name, occurences)
example_data1_diversity## bear lion tiger
## 1 1 2 4Then we can calculate the Simpson Reciprocal Index using the diversity function.
diversity(example_data1_diversity, index="invsimpson")## [1] 2.333333And we can calculate the Chao1 richness estimator (and others by default) with the the specpool function for extrapolated species richness. This function rounds to the nearest whole number so the value will be slightly different that what you’ve calculated above.
specpool(example_data1_diversity)## Species chao chao.se jack1 jack1.se jack2 boot boot.se n
## All 3 3 0 3 0 3 3 0 1In Project 1, you will also see functions for calculating alpha-diversity in the phyloseq package since we will be working with data in that form.
For your sample:
- What is the Simpson Reciprocal Index using the R function?
diversity_candy_diversity =
diversity_candy %>%
select(name, occurences) %>%
spread(name, occurences)
diversity_candy_diversity## Bricks Gummy Bears Hershey Kisses Jujubes M&M Mikes & Ikes Skittles
## 1 18 101 16 24 241 174 187
## Sour Bears Sour Bottle Sour Fruit Sour Hexa Sour Swirl Vines Wine Candy
## 1 3 3 2 6 3 14 9diversity(diversity_candy_diversity, index="invsimpson")## [1] 4.75165## [1] 4.75165- What is the chao1 estimate using the R function?
specpool(diversity_candy_diversity)## Species chao chao.se jack1 jack1.se jack2 boot boot.se n
## All 14 14 0 14 0 14 14 0 1## chao = 14+ Verify that these values match your previous calculations.Part 5: Concluding activity
If you are stuck on some of these final questions, reading the Kunin et al. 2010 and Lundin et al. 2012 papers may provide helpful insights.
- How does the measure of diversity depend on the definition of species in your samples?
The measure of diversity changes if your definition of species changes. For example, if I considered all gummy candy to be one species then the the measure of diversity would be different to if I considered gummy bears and gummy berries to be different species.
- Can you think of alternative ways to cluster or bin your data that might change the observed number of species?
Having same coloured candy represent the same species or having the sour vs sweet candy representing species. The latter would cause the observed number of species to decrease significantly. Even having consistency of candy (like gummy or hard candy) and chocolate being a very braod species will also yield decreased number of species.
- How might different sequencing technologies influence observed diversity in a sample?
That depends on how well the sequencing technology detects differences within the various sequences of different species.
Module 03 Writing Assignment
Prompt
“Discuss the challenges involved in defining a microbial species and how HGT complicates matters, especially in the context of the evolution and phylogenetic distribution of microbial metabolic pathways. Can you comment on how HGT influences the maintenance of global biogeochemical cycles through time? Finally, do you think it is necessary to have a clear definition of a microbial species? Why or why not?”
Introduction
Since microbes are essential to the Earth’s biota and are vital to various biogeochemical cycles that shape the Earth’s environment, researchers have tried to characterize them into species throughout the years. With increase in available genomic sequencing data for microbes, this problem becomes challenging, especially when horizontal gene transfer (HGT) is involved. This essay explores the challenges in defining microbial species as well as how HGT can contribute to this challenge. We dive into why environmental stressors contribute to HGT’s influence in the maintenance of biogeochemical cycles mediated by microbes. And we will study why it is important to have clear definitions of microbial communities to aid researchers and provide reproducibility of species in studies.
Definition of Species in Microbes
The definition of species normally used for plants and animals has a different meaning when it is used to define microbes. This particular challenge stems from the fact that prokaryotes reproduce asexually and are a confounding factor in Ernst Mayr’s Biological Species Concept [1]. Ernst Mayr states that a “species are groups of interbreeding natural populations that are reproductively isolated from other groups” [2]. However, many researchers agree that this definition of species cannot apply to all domains, especially prokaryotes. So how do we study microbial diversity without a set definition of species to characterize the different prokaryotes? Molecular biology has provided a solution to this problem by using 16S ribosomal RNA (rRNA) genes to compare the gene-sequence identity of microbes [1]. Species are normally expected to share at least 97% gene sequence identity for 16S rRNA and at least 70% binding in dsDNA hybridization [1]. Most researchers also like to think of species genomes having a core or backbone of genes shared by a strain of microbes [1]. They might also use phylogenetic trees of core gene sequences to confirm lineage relationships of strains within a particular species [1]. However, strains that have very similar sequences can have differences up to 30% in gene content, and this can be attributed to gene transfer [1].
Complexities of Defining Microbial Species Due to HGT
Microbial species are inherently phylogenetically complex and have many similar characteristics among “different” species. Microbes can have a distinct backbone of genes, but also have a subset of genes that are not associated with the backbone, frequently transferred between prokaryotes through HGT. We can explore this concept with the example given by the Welch et al. paper. Previously, backbone genes were introduced as a set of core genes found in the same strain of microbes, but variations in gene content is linked to HGT. Welch et al. explore the different strains of E. coli that have evolved into both pathogenic and nonpathogenic bacteria [3]. Welch et al. found newly introduced genes separate from an ancestral backbone of genes, most likely caused by various independent horizontal gene-transfer events [3]. This causes the genome structure of the three different strains of E. coli they were studying to carry so-called pathogenicity-associated islands [3]. These pathogenicity islands are determined based on the presumption that pathogenic traits are present in all inserts within the genome [3]. However, they found that similar uropathogenic strains have similar genes found within the same insert site [3]. The frequent movement of these accessory genes that help determine disease and the characteristic of that particular strain of E. coli, causing a challenge for defining species. With more enhanced tools to analyze sequencing data, we can gain more information about the various subsets of accessory genes and can make more informed decisions about characterizing similar strains and species.
The complexity behind defining species can also be attributed to the incredible amount of sequencing data that is available to us, providing us with potentially countless indiscriminate genomic similarities between species further aggravated by horizontal gene transfer. Ciccarelli et al. highlight several challenges that contribute to the difficulty of distinguishing microbial species from each other such as limitations in computing resources handling a massive amount of data and sampling biases of species [4]. The possibility of using different quantitative methods to compare these species make it almost impossible to recreate an identical phylogenetic tree between different users. Also, since disparate sequences cannot always be linked to the same genome, this makes it hard to evaluate the contributions of HGT to species differentiation [2].
HGT’s Influence on Maintenance of Biogeochemical Cycles
Many biogeochemical cycles mediated by microbial species have been maintained with the help of horizontal gene transfer (HGT) due to the need to adapt to changing environments. Selective forces that influence growth of biochemical pathways and networks are often unknown, but we can explore the environmental factors that may have an impact [4]. We know that some of the major biogeochemical fluxes has evolved due to the emergence of microbial life [5]. Falkowski et al. pointed out that distribution of metabolic processes was most likely caused by communal evolution with horizontal gene flow representing the principal mode of gene transfer [5]. A very common biogeochemical cycle studied for its distributed metabolism is the nitrogen cycle. Genes that carry important functions in the nitrogen cycle like ammonia monooxygenase genes which are vital for oxidizing ammonia to hydroxylamine are widely distributed among diverse groups of bacteria and archaea [5]. Nitrogenases, which are responsible for reduction of nitrogen to ammonia, have also been transferred to oxygenic photosynthetic cyanobacteria from an early Archaean source due to HGT [5]. Nitrogenases are also irreversibly inhibited by molecular oxygen, but the gene that encodes this enzyme is highly conserved in the many nitrogen-fixing microbes that live in aerobic environments [5]. To prevent nitrogen fixation from becoming obsolete within the nitrogen cycle, overproduction of the protein in various microbes facilitated by HGT is an evolutionary way to maintain important biogeochemical pathways. These changes are most likely spurred by increasingly challenging environments for the microbes to survive in. Falkowski et al. presume that severe nutritional or bioenergetics selective pressures are most likely major drivers behind horizontally transferred genes [5]. This then facilitates diverse biogeochemical reactions among countless different organisms and environmental contexts [5]. These cycles are important to maintain for the survival of not only microbes but also human life. If a biogeochemical pathway doesn’t survive when the environment changes drastically, it can cause the extinction of the species unable to adapt [5].
It is Necessary for Clear Definitions of Microbial Species
It is certainly necessary for there to be clear definitions for microbial species for the purpose of clarity between researchers and reproducibility of species characterizations. Since microorganisms play essential roles in the cycling of nutrients as well as directly influence the Earth’s climate, therein lies the importance of being able to separate different species. Microbes also make up more than one third of the entire Earth’s biomass [1]. Prokaryotes occur in oceanic and terrestrial surfaces, and the large population size and rapid growth of these prokaryotes provides an enormous capacity for genetic diversity [6]. Their propensity for acquiring diverse genes makes the number of potential prokaryotic species become very large [6]. Different microbes within these large communities have different roles within that community, which can ultimately make interactions between species hard to characterize. We, as researchers, also have a very difficult time condensing and making sense of the molecular and physiological knowledge available for microbes [7]. For us to truly understand the roles of each microbial species, it is important to clearly define them. It is also beneficial for all researchers to share similar information when it comes to microbial species, especially when there are too many variations on how to determine taxonomy of microbes.
Ciccarelli et al. find that numerous groupings and taxonomic entities still remain heavily debated due to increased variety of classifications brought on by our ability to sequence large amounts of genomic data [1]. This is why it is vital to make a universal set of rules to characterize a microbial species or at least have a unified and clear taxonomic tree acknowledged by all. Researchers will benefit from having defined species to further studies that have already been done and understand how microbes shape the world better. Konstantinidis et al. state that it may be premature for us to change the definition because our current knowledge is based on too few samples natural populations [8]. We can apply more stringent standards for species when a more solid understanding of gene content and ecological distinctiveness becomes available [8]. This would allow for a universal understanding of different microbial species as well as a possibility of applying the same set of rules to define species for all researchers.
Conclusion
Overall, it is challenging to differentiate microbial species due to the perceived definition of species, but with the added challenges of HGT and overwhelming sequencing data available, the problem becomes even harder to surmount. Similar but distinct microbial species that undergo HGT can have very few contrasting genes that distinguish them from one another. Biogeochemical cycles, like the important nitrogen cycle, have important genes that are transferred by HGT to confer survival of microbes which further complicates lineage determination among those microbes. However, it is still important to draw clear division between species, for uniformity reasons and reproducibility of data and findings. Next steps would include determining the robustness of methods used to determine which microbes are grouped together as a certain species. If we continue using 16S analysis and sequencing data, should we explore which methods available for binning of metagenomic datasets is the most comprehensive for microbes?
Module 03 references
Welch R. A. et al.. (2002). Extensive mosaic structure revealed by the complete genome sequence of uropathogenic Escherichia coli. Proc. Natl. Acad. Sci. U.S.A. 99, 17020–17024.
Torres-Beltran, Monica et al. 2017
Thompson, Janelle R. et al. 2005
Callahan, Benjamin J. et al. 2017
Additional Readings
Loman, Nicholas J. et al. 2012
Schloss, Patrick D. et al. 2009
Module 03 Writing Assignment References
[1] [Doolittle, W.F.](https://microbiologybytes.wordpress.com/2006/11/13/what-is-a-bacterial-species/) “What is a bacterial species?” Microbiology Today, 13 Nov. 2006.
[2] [de Queiroz, Kevin.](http://www.pnas.org/content/102/suppl_1/6600) “Ernst Mayr and the modern concept of species.” PNAS, volume 102, 03 May 2005, pp. 6600-6607.
[3] [Welch, R.A., et al.](https://www.ncbi.nlm.nih.gov/pubmed/12471157) “Extensive Mosaic Structure Revealed by the Complete Genome Sequence of Uropathogenic Escherichia Coli.” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 26, 24 Dec. 2002, p. 17020.
[4] [Ciccarelli, Francesca D., et al.](https://www.ncbi.nlm.nih.gov/pubmed/16513982) “Towards Automatic Reconstruction of a Highly Resolved Tree of Life.” Science, vol. 311, no. 5765, 03 Mar. 2006, pp. 1283-1287.
[5] [Falkowski, Paul G., et al.](http://science.sciencemag.org/content/320/5879/1034.full) “The Microbial Engines That Drive Earth’s Biogeochemical Cycles.” Science, vol. 320, no. 5879, 23 May 2008, pp. 1034-1039.
[6] [Whitman, William B., et al.](http://www.pnas.org/content/95/12/6578) “Prokaryote: The unseen majority.” National Academy of Sciences, vol. 95, no. 12, 09 Jun. 1998, pp. 6578-6583.
[7] [von Mering, C., et al.](https://www.ncbi.nlm.nih.gov/pubmed/17272687) “Quantitative Phylogenetic Assessment of Microbial Communities in Diverse Environments.” Science, vol. 315, no. 5815, 23 Feb. 2007, pp. 1126-1130.
[8] [Konstantinidis, Konstantinos T. et al.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1764935/) “The Bacterial Species Definition in the Genomic Era.” Philosophical Transactions of the Royal Society B: Biological Sciences 361.1475 (2006): 1929–1940. PMC. Web. 18 Apr. 2018.
Project 1 - Group 6
Abstract
Analysis of sequences obtained from Saanich Inlet using mothur and QIIME2 revealed peak community alpha-diversity at a depth of approximately 100 m which contains approximately 38 uM of dissolved oxygen. Lowest diversity was observed at greater depth and with lower oxygen levels. Further analysis of taxonomic levels revealed Proteobacteria as the most abundant phylum within all samples. In order to investigate how microbial communities differ across depth and oxygen gradients within the Saanich Inlet, we focused on the phylum Chloroflexi. Analysis revealed a positive correlation between depth and Chloroflexi abundance. This correlation was significant only when analysis was based on QIIME2-generated ASVs, but not mothur-generated OTUs. In addition, there exists a negative correlation between oxygen concentration and Chloroflexi abundance. Similarly, significance was only reported using QIIME2-generated ASVs. The analysis of data using QIIME2 identified four classes within the phylum Chloroflexi (Dehalococcoidia, Anaerolineae, SAR202 and JG30-KF-CM66) while mothur identified two classes within this phylum (Anaerolineae and SAR202). Changes in the abundance of OTUs and ASVs were correlated with depth and oxygen concentration. No correlation was found to be significant. The abundances of 24 (of 34) OTUs and 38 (of 47) ASVs were positively correlated with depth, while all OTUs and 46 ASVs were negatively correlated with oxygen concentration. However, the presence of seeming outliers in data analyzed by both mothur and QIIME2 may have biased the trend line and reduced model significances. Although, the absence of sufficient data limits us from classifying them as outliers. Lastly, changes in the abundances of some OTUs and ASVs were found to be significantly positively correlated with hydrogen sulfide levels. The differences observed between mothur and QIIME2 indicate the role played by the choice of pipeline to analyze results.
Introduction
Saanich Inlet is a seasonally anoxic fjord [1] located between Vancouver Island and the Saanich Peninsula. It is 24 km long and has a basin of up to 234 meters in depth [2]. It has a 75-meter sill which acts to protect the deeper waters [3]. Because of this sill and the constantly high input of organic material from freshwater discharge and primary production in surface waters, its conditions below 110 meters are anoxic [3]. Oxygen is replenished dependent on the season, mostly in the fall, which modifies the oxygen gradient and thereby the environmental conditions for the microbial community that inhabit the inlet [3]. Dissolved oxygen increases gradually from a minimum concentration at higher depth up to its peak concentration at the surface due to phytoplankton metabolism and atmospheric surface waters gas exchange [3]. Nitrate reduction by denitrifiers happens mostly in the deep water following oxygenation [3]. This results in a steep nitrate gradient when looking at the different depths within the fjord [3]. A study by Zaikova et al. found that microbial diversity was highest in the hypoxic transition area and that it decreases within the anoxic basin waters [1]. It is vital to study the roles of various microorganisms within Saanich Inlet in order to understand how they affect environmental conditions like greenhouse gases, methane, and denitrogenation on a larger scale in the world’s oceans [3].
Oxygen minimum zones (OMZ) are places within the ocean where the saturation of oxygen is the lowest, typically occurring at depths of about 200 to 1000 meters [4]. These OMZs are normally found along the western boundaries of continents, which is where upwelling can bring nutrient rich water from deep within the ocean up to the surface [5]. They can also be found in coastal basins where restricted circulation can restrict mixing of deep and surface waters [5]. Not only that, but human activity can affect the size of periodic dead zones (in coastal ocean and enclosed basins) by causing runoff from agriculture to mix with marine waters - this process is called eutrophication [5]. Some important examples of this include dead zones in the Mississippi River Delta and Chesapeake Bay in America. The Saanich Inlet in British Columbia is a glacially carved fjord unlike these other zones where its entrance sill restricts oxygen rich water from entering the basin [5].
Operational Taxonomic Units (OTUs) are defined as clusters of organisms that have been grouped based on DNA sequence similarity of a specific DNA segment known as a taxonomic marker gene [6]. The grouped DNA sequences differ by less than a fixed and arbitrary sequence dissimilarity threshold, often 3% [7]. This process of clustering on a specific DNA segment, known as DNA barcoding, allows for rapid, targeted, and high throughput analysis of genetic variation in a specific genomic region such as 16s/18s rRNA sequences, leading to large scale characterization of microbial communities [6, 8]. However, new recent amplicon sequence variants (ASVs) methods have been developed with finer resolution and are independent of dissimilarity thresholds that have been used to define OTUs. ASV methods have shown higher specificity and sensitivity in comparison to OTU methods as they distinguish sequence variants as small as single nucleotides and denoise the sequences by discriminating biological sequences from errors. This is done based on the expectation that biological sequences are more abundant and more repeatedly observed than error-containing sequences [7].
Using OTU and ASV data for samples collected from the Saanich Inlet, we investigated how microbial communities differ across depth and oxygen gradients within the Saanich Inlet, with a particular focus on the phylum Chloroflexi. We found Chloroflexi of interest because its members are highly abundant in marine sediments [9] and present a broad spectrum of metabolic characteristics such as anoxygenic photosynthesis [10], obligate aerobic and anaerobic heterotrophy [11], and even predation with a gliding motility [12]. Like many other microbes, members of Chloroflexi can be a challenge to grow in culture, with some classes yet to be cultured successfully, which has made characterizing their metabolisms a challenge [13, 21]. However, new sequencing technologies have made it possible to analyze the genome of and characterize these uncultured microbes [13-21].
In our Saanich Inlet data, we were able to identify four classes within the phylum Chloroflexi: Dehalococcoidia, Anaerolineae, SAR202, and JG30-KF-CM66. Members of the class Dehalococcoidia are widely distributed throughout marine sediments [13] and anoxic deep waters [14]. Dehalococcoidia grow via anaerobic organohalide respiration and are extensively studied for their potential in the bioremediation of chloride-contaminated water and soil [13, 14]. As for the class Anaerolineae, despite its members being prevalent in various ecosystems, only a few strains have been successfully cultured [15]. Anaerolineae compose one of the core populations of anaerobic bacteria involved in anaerobic digestion and possess key genes for catalyzing cellulose hydrolysis [16].The SAR202 cluster was one of the earliest discoveries of marine bacteria which inhabited the aphotic zone [17], and since then SAR202 has been found to be ubiquitous throughout the deep ocean [18]. Members of SAR202 are involved in metabolizing organosulfur compounds and likely play a major role in sulfur cycling [19]. JG30-KF-CM66 is a relatively uncharacterized clade of acidobacteria, but it has been identified in soft coal slags [20] and anoxic ocean water [21]. The characteristics of each of these classes impact how the spatial distribution of the Chloroflexi phylum differs within Saanich Inlet. We also set out to determine if and how different sequence processing pipelines, specifically mothur and QIIME2, would impact these biological conclusions.
Methods
Sample Collection and Processing
Water samples from 16 depths (10-200m) from cruise 72 were collected at station S3 (48°35.500 N, 123°30.300 W) onboard MSV John Strickland. Geochemical and multi-omic information, which included 16S rRNA gene amplicon sequences (V4-5 hypervariable regions) and dissolved O2, were extracted for each depth [22, 23]. Data from 7 depths (10, 100, 120, 135, 150, 165, 200m) were further analyzed. Dissolved O2 was measured onboard by the Sea-Bird SBE 43 Photosynthetically Active Radiation sensor [22]. 2L of water sample at each depth were filtered onto 0.22μm Strerivex filters, and stored until amplicon sequencing, which was carried out on the Illumina MiSeq platform at the Joint Genome Institute. Base qualities were encoded in Phred33, and primers 515F and 806R were used for 16S rRNA gene amplification [23].
Data Processing
Reads were independently processed using mothur and QIIME2 based pipelines, which cluster sequences based on OTUs and ASVs, respectively. Resultant data were constructed as phyloseq objects for downstream analysis in R. For a comprehensive outline and description of commands used for each pipeline, refer to the R markdown documents provided by Kim Dill-McFarland (2018; mothur pipeline) and Julia Beni (2018; QIIME2 pipeline).
mothur pipeline
Sequenced reads were first assembled into contigs, which were screened and de-duplicated so that the remainder 1) were between 200bp and 600bp long, 2) had fewer than 8 homopolymers and 3) had no ambiguous bases.
Configs were trimmed such that they would only align to bases 10368 to 25434 in the SILVA database (release 128), and uninformative bases were removed. Resultant sequences were de-duplicated again.
Sequences were then pre-clustered and clustered de novo (using 97% sequence similarity) to determine the final OTUs. Chimeric sequences were also filtered away.
Clusters were classified using the SILVA database, and resulting taxonomies were condensed.
QIIME2 pipeline
Reads were demultiplexed and imported into QIIME2. Per-base read qualities were visualized to determine downstream trim parameters.
ASVs were generated using the Dada2 protocol using custom trim parameters for quality control. Resultant ASVs were classified using the SILVA database (release 119) using a 99% similarity threshold.
The ASV table was then converted to text format used to create a phyloseq object.
Data Analysis
Analysis was completed in R v3.4.3 [24] using the following packages.
library(tidyverse)
library(phyloseq)
library(ggplot2)
library(dplyr)
library(stringr)
library(magrittr)
library(knitr)
library(gridExtra)
library(grid)
library(randomcoloR)Alpha-diversities of clusters identified by mothur and QIIME2 from each sample were measured by the Shannon diversity index and the Chao1 richness estimator. Alpha-diversities were plotted against sample depth and oxygen concentration for both clustering methods, and were fitted using local polynomial regression models where appropriate. Relative abundances of all phylum level classifications produced by mothur and QIIME2 were also plotted for each sample.
Relative abundances of Chloroflexi OTUs and ASVs amongst all clusters were plotted, both as a whole and individually, across depth and oxygen gradients. Significances of correlations between these variables were based on linear regression models, as all variables are continuous and there is a lack of evidence to suggest curvilinear relationships between them.
Data cleaning
# Generate random colours for use in figures.
palette <- distinctColorPalette(40) Data were loaded into R and samples normalized to 100,000 sequences per sample.
load("mothur_phyloseq.RData")
load("qiime2_phyloseq.RData")
# Random seed set for reproducibility
set.seed(4831)
# Data normalized to 100,000 sequences per sample
m.norm = rarefy_even_depth(mothur, sample.size=100000)
q.norm = rarefy_even_depth(qiime2, sample.size=100000)Relative abundance percentages were calculated for the data.
m.percent = transform_sample_counts(m.norm, function(x) 100 * x/sum(x))
q.percent = transform_sample_counts(q.norm, function(x) 100 * x/sum(x))The phylum Chloroflexi was chosen.
phylum_name_mothur = "Chloroflexi"
phylum_name_qiime2 = "D_1__Chloroflexi"Results
Change of microbial community structure with depth and oxygen concentration
Alpha-diversity
Shannon diversity index and Chao1 were calculated for the total microbial community across depth and oxygen concentration gradients for both mothur and QIIME2.
# Alpha-diversity of total community for mothur
# Calculate Chao1 and Shannon
m.alpha = estimate_richness(m.norm, measures = c("Chao1", "Shannon"))
# Combine Chao1 and Shannon data with the rest of the geochemical data into 1 data frame
m.meta.alpha = full_join(rownames_to_column(m.alpha),
rownames_to_column(data.frame(m.percent@sam_data)), by = "rowname")
# Save plots for the different combinations (Shannon vs Chao1 across depth vs oxygen)
m.shannon.depth.plot <- m.meta.alpha %>%
ggplot() +
geom_point(aes(x=Depth_m, y=Shannon)) +
geom_smooth(method='auto', aes(x=as.numeric(Depth_m), y=Shannon)) +
labs(title="mothur", y="Shannon diversity index", x=NULL)
m.chao1.depth.plot <- m.meta.alpha %>%
ggplot() +
geom_point(aes(x=Depth_m, y=Chao1)) +
geom_smooth(method='auto', aes(x=as.numeric(Depth_m), y=Chao1)) +
labs(title="mothur", y="Chao1 richness estimator", x="Depth (m)")
m.shannon.o2.plot <- m.meta.alpha %>%
ggplot() +
geom_jitter(aes(x=O2_uM, y=Shannon), width = 5, shape = 1) +
labs(title="mothur", y="Shannon diversity index", x=NULL)
m.chao1.o2.plot <- m.meta.alpha %>%
ggplot() +
geom_jitter(aes(x=O2_uM, y=Chao1), width = 5, shape = 1) +
labs(title="mothur", y="Chao1 richness estimator", x="Oxygen (uM)")
# Alpha-diversity of total community for QIIME2
# Calculate Chao1 and Shannon
q.alpha = estimate_richness(q.norm, measures = c("Chao1", "Shannon"))
# Combine Chao1 and Shannon data with the rest of the geochemical data into 1 data frame
q.meta.alpha = full_join(rownames_to_column(q.alpha),
rownames_to_column(data.frame(q.percent@sam_data)), by = "rowname")
# Save plots for the different combinations (Shannon vs Chao1 across depth vs oxygen)
q.shannon.depth.plot <- q.meta.alpha %>%
ggplot() +
geom_point(aes(x=Depth_m, y=Shannon)) +
geom_smooth(method='loess', aes(x=as.numeric(Depth_m), y=Shannon)) +
labs(title="QIIME2", y=NULL, x=NULL)
q.chao1.depth.plot <- q.meta.alpha %>%
ggplot() +
geom_point(aes(x=Depth_m, y=Chao1)) +
geom_smooth(method='loess', aes(x=as.numeric(Depth_m), y=Chao1)) +
labs(title="QIIME2", y=NULL, x="Depth (m)")
q.shannon.o2.plot <- q.meta.alpha %>%
ggplot() +
geom_jitter(aes(x=O2_uM, y=Shannon), width = 5, shape = 1) +
labs(title="QIIME2", y=NULL, x=NULL)
q.chao1.o2.plot <- q.meta.alpha %>%
ggplot() +
geom_jitter(aes(x=O2_uM, y=Chao1), width = 5, shape = 1) +
labs(title="QIIME2", y=NULL, x="Oxygen (uM)")# Plotting alpha-diversities versus depth
grid.arrange(m.shannon.depth.plot, q.shannon.depth.plot, m.chao1.depth.plot, q.chao1.depth.plot, ncol=2)
Figure 1 Alpha-diversity of samples collected at Saanich Inlet across depth. Sequence processing was done with both mothur and QIIME2. Points were fitted using local polynomial regression fitting. 95% confidence band is displayed in grey.
The same patterns of alpha-diversity (Shannon diversity index and the Chao1 richness estimator) can be observed across depth for both mothur and QIIME2 (Figure 1). There is a slightly lower diversity in surface waters (0m) compared to 100m depth. Peak diversity is reached at ~100-120m then diversity decreases with greater depth, with a slight increase at 200m for all but Shannon diversity index for QIIME2.
Note, however, that despite the similarity in the alpha-diversity pattern, the comparison of mothur versus QIIME2 shows difference: across all depths, mothur OTU analysis resulted in a lower alpha-diversity than the QIIME2 ASV analysis when measured with the Shannon diversity index and a higher alpha-diversity than the QIIME2 ASV analysis when measured with Chao1.
# Plotting alpha-diversities versus oxygen concentration
grid.arrange(m.shannon.o2.plot, q.shannon.o2.plot, m.chao1.o2.plot, q.chao1.o2.plot, ncol=2)
Figure 2 Alpha-diversity of samples collected at Saanich Inlet across oxygen concentration. Sequence processing was done with both mothur and QIIME2. Points are jittered to show 2 sets of 2 closely overlapping points revealed by the mothur pipeline.
Looking at Shannon diversity across oxygen concentration (Figure 2), we find that at equivalent depths QIIME2 has a greater diversity than mothur. However, the pattern exhibited by both mothur and QIIME2 data is still similar. The three lowest diversity points are at an oxygen concentration of 0 uM, while the highest diversity is found at an oxygen concentration of ~38 uM. The band of 95% confidence intervals was not plotted due to the lack of data between ~38 uM and ~217 uM of oxygen.
Comparing Chao1 at different oxygen levels for mothur and QIIME2 shows that the patterns somewhat differ. While the three lowest diversity points are still at 0 uM of oxygen, for mothur the highest diversity in terms of Chao1 is at an oxygen concentration of ~38 uM, while for QIIME2 it is at an oxygen concentration of ~32 uM. For both, oxygen concentration of ~217 uM shows a notable decrease in diversity. Chao1 exhibited a relatively greater drop at ~217 uM of oxygen compared to Shannon.
Taxonomic community composition
# Save plot for phylum abundance composition for mothur
m.phyla.plot = m.percent %>%
plot_bar(fill="Phylum")+
geom_bar(aes(fill=Phylum), stat="identity")+
labs(y="Abundance (%)")+
scale_fill_manual(values=palette)+
theme(legend.text=element_text(size=11))
# Save plot for phylum abundance composition for QIIME2
q.phyla.plot = q.percent %>%
plot_bar(fill="Phylum")+
geom_bar(aes(fill=Phylum), stat="identity")+
labs(y="Abundance (%)")+
scale_fill_manual(values=palette)+
theme(legend.text=element_text(size=11))
Figure 3 Relative distribution of phyla across samples from Saanich Inlet when clustered as OTUs using mothur.
Figure 4 Relative distribution of phyla across samples from Saanich Inlet when clustered as ASVs using QIIME2.
Mothur and QIIME2 identified 28 and 29 taxa, respectively, at the phylum level (Figures 3 & 4). Out of these identified phyla in both mothur and QIIME2, ~4 dominated the community composition in terms of abundance: Proteobacteria, Bacteroidetes, Thaumarchaeota and Actinobacteria (from most to less abundant). Other phyla that are noticeably more abundant include Cyanobacteria, Deferribacteres, Euryarchaeota, Firmiucutes, Gemmatimonadetes, Marinimicrobia, Nitrospinae, Planctomycetes and Verrucomicrobia. Our phylum of interest, Chloroflexi, makes up from 0 to 6% of the microbial community in the collected samples depending on depth.
Chloroflexi abundance with depth and oxygen concentration
# Get summary of linear model statistics for Chloroflexi abundance across depth for mothur
m.chlor.lm = m.norm %>%
subset_taxa(Phylum==phylum_name_mothur) %>%
tax_glom(taxrank = 'Phylum') %>%
psmelt() %>%
lm(Abundance ~ Depth_m, .) %>%
summary()
# Get summary of linear model statistics for Chloroflexi abundance across depth for QIIME2
q.chlor.lm = q.norm %>%
subset_taxa(Phylum==phylum_name_qiime2) %>%
tax_glom(taxrank = 'Phylum') %>%
psmelt() %>%
lm(Abundance ~ Depth_m, .) %>%
summary()
# Make a data frame for linear model statistics data for depth
taxon.abundance = data.frame("Estimate" = numeric(0), "Std. Error"= numeric(0),"t value"= numeric(0),"Pr(>|t|)"= numeric(0))
taxon.abundance <- rbind(taxon.abundance, m.chlor.lm$coefficients["Depth_m",])
taxon.abundance <- rbind(taxon.abundance, q.chlor.lm$coefficients["Depth_m",])
rownames(taxon.abundance) <- (c("mothur", "QIIME2"))
colnames(taxon.abundance) <- (c("Estimate", "Std. Error","t value","Pr(>|t|) (p-value)"))
# Make a table for the data
kable(taxon.abundance,caption="Table 1 Correlation Data of Chloroflexi Phylum across Depth")| Estimate | Std. Error | t value | Pr(>|t|) (p-value) | |
|---|---|---|---|---|
| mothur | 1.327529 | 0.6389862 | 2.077554 | 0.0923485 |
| QIIME2 | 2.622128 | 0.4212043 | 6.225311 | 0.0015644 |
# Filter our phylum, group by sample and summarize abundance across depth for mothur
m.abd.depth.plot <- m.percent %>%
subset_taxa(Phylum==phylum_name_mothur) %>%
psmelt() %>%
group_by(Sample) %>%
summarize(Abundance_sum=sum(Abundance), Depth_m=mean(Depth_m)) %>%
# Use the data in a plot (abundance versus depth)
ggplot() +
geom_point(aes(x=Depth_m, y=Abundance_sum)) +
geom_smooth(method='lm', aes(x=as.numeric(Depth_m), y=Abundance_sum)) +
labs(title="mothur", y="Abundance (%)", x="Depth (m)")
# Filter our phylum, group by sample and summarize abundance across depth for QIIME2
q.abd.depth.plot <- q.percent %>%
subset_taxa(Phylum==phylum_name_qiime2) %>%
psmelt() %>%
group_by(Sample) %>%
summarize(Abundance_sum=sum(Abundance), Depth_m=mean(Depth_m)) %>%
# Use the data in a plot (abundance versus depth)
ggplot() +
geom_point(aes(x=Depth_m, y=Abundance_sum)) +
geom_smooth(method='lm', aes(x=as.numeric(Depth_m), y=Abundance_sum)) +
labs(title="QIIME2", y=NULL, x="Depth (m)")# Get summary of linear model statistics for Chloroflexi abundance across oxygen concentrations for mothur
m.chlor.lm.ox = m.norm %>%
subset_taxa(Phylum==phylum_name_mothur) %>%
tax_glom(taxrank = 'Phylum') %>%
psmelt() %>%
lm(Abundance ~ O2_uM, .) %>%
summary()
# Get summary of linear model statistics for Chloroflexi abundance across oxygen concentrations for QIIME2
q.chlor.lm.ox = q.norm %>%
subset_taxa(Phylum==phylum_name_qiime2) %>%
tax_glom(taxrank = 'Phylum') %>%
psmelt() %>%
lm(Abundance ~ O2_uM, .) %>%
summary()
# Make a data frame for linear model statistics data for oxygen concentrations
taxon.abundance.ox = data.frame("Estimate" = numeric(0), "Std. Error"= numeric(0),"t value"= numeric(0),"Pr(>|t|)"= numeric(0))
taxon.abundance.ox <- rbind(taxon.abundance.ox, m.chlor.lm.ox$coefficients["O2_uM",])
taxon.abundance.ox <- rbind(taxon.abundance.ox, q.chlor.lm.ox$coefficients["O2_uM",])
rownames(taxon.abundance.ox) <- (c("mothur", "QIIME2"))
colnames(taxon.abundance.ox) <- (c("Estimate", "Std. Error","t value","Pr(>|t|) (p-value)"))
# Make a table for the data
kable(taxon.abundance.ox,caption="Table 2 Correlation Data of Chloroflexi Phylum across Oxygen Concentration")| Estimate | Std. Error | t value | Pr(>|t|) (p-value) | |
|---|---|---|---|---|
| mothur | -0.750471 | 0.5865861 | -1.279387 | 0.2569128 |
| QIIME2 | -1.731996 | 0.5762708 | -3.005525 | 0.0299088 |
# Filter our phylum, group by sample and summarize abundance across oxygen concentration for mothur
m.abd.o2.plot <- m.percent %>%
subset_taxa(Phylum==phylum_name_mothur) %>%
psmelt() %>%
group_by(Sample) %>%
summarize(Abundance_sum=sum(Abundance), O2_uM=mean(O2_uM)) %>%
# Use the data in a plot (abundance versus oxygen)
ggplot() +
geom_point(aes(x=O2_uM, y=Abundance_sum)) +
geom_smooth(method='lm', aes(x=as.numeric(O2_uM), y=Abundance_sum)) +
labs(title="mothur", y="Abundance (%)", x="O2 (uM)")
# Filter our phylum, group by sample and summarize abundance across oxygen concentration for QIIME2
q.abd.o2.plot <- q.percent %>%
subset_taxa(Phylum==phylum_name_qiime2) %>%
psmelt() %>%
group_by(Sample) %>%
summarize(Abundance_sum=sum(Abundance), O2_uM=mean(O2_uM)) %>%
# Use the data in a plot (abundance versus oxygen)
ggplot() +
geom_point(aes(x=O2_uM, y=Abundance_sum)) +
geom_smooth(method='lm', aes(x=as.numeric(O2_uM), y=Abundance_sum)) +
labs(title="QIIME2", y=NULL, x="O2 (uM)")# Plotting Chloroflexi abundance across depth
grid.arrange(m.abd.depth.plot, q.abd.depth.plot, ncol=2)
Figure 5 Relative abundance of Chloroflexi across depth for samples collected at Saanich Inlet. Points are fitted using linear models. 95% confidence bands are displayed in grey.
# Plotting Chloroflexi abundance across oxygen concentration
grid.arrange(m.abd.o2.plot, q.abd.o2.plot, ncol=2)
Figure 6 Relative abundance of Chloroflexi across oxygen concentration for samples collected at Saanich Inlet. Points are fitted using linear models. 95% confidence bands are displayed in grey.
Linear regression analysis of Chloroflexi relative abundance across depth revealed variations between mothur’s OTU and QIIME2’s ASV clustering (Figure 5). Abundance of ASV clusters revealed a significant correlation with depth (p<0.05), while OTU clusters did not (Table 1). Both correlations were found to be positive.
Similarly, linear regression analysis of Chloroflexi relative abundance across oxygen concentration (Figure 6) revealed a significant correlation of oxygen concentration with ASV clusters (p<0.05), but not with OTU clusters (Table 2). Both correlations were found to be negative.
Richness within the Chloroflexi phylum
# Get OTUs taxa
m.tax_table = data.frame(m.norm@tax_table)
# Filter out OTUs with our phylum name
m.filtered = m.tax_table %>%
rownames_to_column('OTU') %>%
filter(Phylum==phylum_name_mothur) %>%
column_to_rownames('OTU')
# Get total number of OTUs in Chloroflexi
m.rownumber = nrow(m.filtered)
# Get names of classes in OTUs
m.classes = m.filtered %>%
select('Class') %>%
unique %>%
summarise(Classes = toString(Class))
# Get ASVs taxa
q.tax_table = data.frame(q.norm@tax_table)
# Filter out ASVs with our phylum name
q.filtered = q.tax_table %>%
rownames_to_column('ASV') %>%
filter(Phylum==phylum_name_qiime2) %>%
column_to_rownames('ASV')
# Get total number of ASVs in Chloroflexi
q.rownumber = nrow(q.filtered)
# Get names of classes in ASVs
q.classes = q.filtered %>%
select('Class') %>%
unique %>%
summarise(Classes = toString(Class))For Chloroflexi, the number of OTUs was found to be 34, and the number of ASVs was found to be 47. The OTUs represent classes: SAR202_clade, Anaerolineae, while the ASVs represent classes: D_2__JG30-KF-CM66, D_2__Anaerolineae, D_2__uncultured, D_2__Dehalococcoidia, D_2__SAR202 clade.
Change of abundances of OTUs/ASVs within the Chloroflexi phylum with depth and oxygen concentration
# Example for linear model
# Make data frame that holds linear model statistics data (OTUs depth)
otu_stats = data.frame("Estimate" = numeric(0), "Std. Error"= numeric(0),"t value"= numeric(0),"Pr(>|t|)"= numeric(0))
for (otu in row.names(m.filtered)){
linear_fit = m.norm %>%
psmelt() %>%
filter(OTU==otu) %>%
lm(Abundance ~ Depth_m, .) %>%
summary()
otu_data = linear_fit$coefficients["Depth_m",]
otu_stats <- rbind(otu_stats, otu_data)
}
colnames(otu_stats)<- (c("Estimate", "Std. Error","t value","Pr(>|t|) (p-value)"))
# Add OTU names
row.names(otu_stats) <- row.names(m.filtered)
# Add class and genus names
otu_stats = cbind(data.frame(Class = m.filtered$Class), Genus = m.filtered$Genus, otu_stats)
# Sort data frame by linear model slope (estimate)
sorted = arrange(rownames_to_column(otu_stats),Estimate)%>% column_to_rownames(var="rowname")
# Save data table in variable
lm.depth.otus = kable(sorted,caption="Table A1 Correlation data of Chloroflexi OTUs Abundance with Depth")# Example for correlation graph
# Correlation graph for Chloroflexi OTUs percentage abundance across depth, first filtered for Chloroflexi, then plotted
m.percent %>%
subset_taxa(Phylum==phylum_name_mothur) %>%
psmelt() %>%
ggplot() +
geom_point(aes(x=Depth_m, y=Abundance)) +
geom_smooth(method='lm', aes(x=Depth_m, y=Abundance)) +
facet_wrap(~OTU, scales="free_y") +
labs(x = "Depth (m)", y = "Abundance (%)") +
theme(axis.text.x = element_text(angle = 90))
Figure 7 Relative abundances of Chloroflexi OTUs across depth for samples collected at Saanich Inlet. Points are fitted using linear models. 95% confidence bands are displayed in grey. None of the correlations were found to be significant. The exact p-values for each fit can be found in Table A1.
Figure 8 Relative abundances of Chloroflexi ASVs across depth for samples collected at Saanich Inlet. Points are fitted using linear models. 95% confidence bands are displayed in grey. None of the correlations were found to be significant. The exact p-values for each fit can be found in Table A2.
Figure 9 Relative abundances of Chloroflexi OTUs across oxygen concentration for samples collected at Saanich Inlet. Points are fitted using linear models. 95% confidence bands are displayed in grey. None of the correlations were found to be significant. The exact p-values for each fit can be found in Table A3.
Figure 10 Relative abundances of Chloroflexi ASVs across oxygen concentration for samples collected at Saanich Inlet. Points are fitted using linear models. 95% confidence bands are displayed in grey. None of the correlations were found to be significant. The exact p-values for each fit can be found in Table A4.
Linear model statistics were performed for the abundance of each OTU and ASV in relation to depth and oxygen concentration (Appendix A Table A1-A4). The linear models were subsequently plotted (Figures 7-10). No significant correlations were found between any individual OTUs/ASVs abundance and depth or oxygen concentration (p > 0.05 for all).
Although none of the correlations were significant, mothur and QIIME2 showed similar trends. For mothur ten of the 34 OTUs had negative correlation between abundance and depth (the rest positive), while for QIIME2 nine of the 47 ASVs had negative correlation between abundance and depth (the rest positive). This was while for abundance versus oxygen concentration, for mothur all OTUs had negative correlation, and for QIIME2 all but one ASVs had negative correlation.
Change of abundances of OTUs/ASVs within the Chloroflexi phylum with hydrogen sulfide concentration
Figure 11 Relative abundances of Chloroflexi OTUs across hydrogen sulfide concentration for samples collected at Saanich Inlet. Points are fitted using linear models. 95% confidence bands are displayed in grey. Significance of each fit can be found in Table A5.
Figure 12 Relative abundances of Chloroflexi ASVs across hydrogen sulfide concentration for samples collected at Saanich Inlet. Points are fitted using linear models. 95% confidence bands are displayed in grey. Significance of each fit can be found in Table A6.
Linear model statistics were performed for the abundance of each OTU and ASV in relation to hydrogen sulfide concentration (Appendix A Table A5 & A6). The linear models were subsequently plotted (Figures 11 & 12). Significant positive correlations between abundance and hydrogen sulfide concentration after p-adjusting were found for 15 and 19 individual OTUs and ASVs, respectively. For OTUs and ASVs, p-values were <0.05. Classes associated with OTUs and ASVs positively correlating with hydrogen sulfide concentration include Anaerolineae, Dehalococcoidia and the SAR202 clade.
QIIME2 identified 9 more individual members of Chloroflexi that significantly and positively correlate with hydrogen sulfide concentration compared to mothur. In addition, the significance of the results and the positive correlation with hydrogen sulfide concentration were generally greater with QIIME2 than mothur.
Discussion
The Saanich Inlet provides a good model for the study of oxygen minimum zones and the microbial dynamics that shape them. The microbial diversity analyses we carried out with the two bioinformatic pipelines mothur and QIIME2 resulted in mostly similar patterns, but there were differences when it came to the details. Both pipelines found that the peak Shannon diversity index was at 100 m, however, the peak Chao1 richness estimates for the total community was at a 100 m for mothur and 120 m for QIIME2. We see the same discrepancy between mothur and QIIME2 and their Chao1 richness estimates when looking at alpha-diversity across dissolved oxygen concentrations (Figure 2). As for discrepancies between Shannon diversity and Chao1 richness, we found that at the dissolved oxygen concentration of 217 μm Chao1 was relatively lower than Shannon for both mothur and QIIME2. The lower value of Chao1 compared to Shannon at ~217uM could indicate increased species evenness despite reduced species diversity because Chao1 does not take into account evenness while Shannon does. The two pipelines also agreed that Saanich Inlet is dominated by only 4-5 phyla, with the phylum Chloroflexi making up 0-6% of the microbial community depending on the sample depth.
The phylum Chloroflexi contains bacteria with different metabolic characteristics, such as aerobic thermophiles, anoxygenic phototrophs, and anaerobic halorespirers which use halogenated organics as electron acceptors [10, 11]. In our analysis, we found that Chloroflexi abundance was positively correlated with depth (Table 1). This may be a result of oxygen concentrations decreasing with depth: a negative correlation between Chloroflexi abundance and oxygen concentrations was also found (Table 2). Interestingly, these results were determined to only be significant in the QIIME2 analysis, but not the mothur analysis. This discrepancy is discussed later and points out that the analysis method, in this case, plays a role in whether our analysis results are significant or not. The results indicate a preference for members of Chloroflexi to inhabit anoxic habitats at depth within Saanich Inlet, which is supported by previously mentioned research on this phylum [11, 16, 21]. Since Chloroflexi encompasses such vastly different classes of bacteria with disparate metabolistic behavior, it is important to note that the diverse classes within this phylum have different requirements for oxygen content, where some of them also thrive in oxic environments [11, 16, 21]. This may explain why the abundance vs. depth and oxygen concentration results with mothur were not significant (Table 1 & 2). However, we only identified a few classes within Chloroflex from our data, and each of those classes appears to be anaerobic [14-18], so the potential outliers might be a result of something else, such as other nutrients. Anoxic oceanic zones such as the deep waters of Saanich Inlet may provide an environment in which anaerobic members of Chloroflexi can be more competitive than other phyla and dominate the microbial population.
The richness within Chloroflexi highly depends on which bioinformatic pipeline is used for the analysis. Mothur was only able to identify 34 OTUs occupying 2 classes within Chloroflexi, while QIIME2 was able to identify 47 ASVs and 5 classes within Chloroflexi. However, QIIME2 was unable to identify any genera while mothur was (Table A1-3). Therefore, the required depth into the taxonomic tree may dictate which pipeline should be used in future analyses. Correlations between individual OTUs and ASVs within Chloroflexi against depth or oxygen concentration were found to be insignificant (Appendix A Table 1-4). Linear models in Figures 7-10 show similar trends between the individual OTUs and ASV with depth and oxygen concentration, however there are glaring single outlier defying any correlation between the data points. These outliers are present in all analyses but do not occur at the same depths or oxygen levels for the various ASVs and OTUs studied. This may mean that there is no pattern or rationale for their occurrences. More data would allow to either legitimize these data points or confirm them as outliers to either integrate them or exclude them from the results.
Since members of the Chloroflexi phylum have been associated with sulfur cycling, the hydrogen sulfide levels in Saanich Inlet were investigated in relation to the abundance of members of this phylum [19]. Strong positive and significant correlations were found between various members of the Chloroflexi phylum and hydrogen sulfide concentrations. Classes correlating with hydrogen sulfide levels include Anaerolineae, Dehalococcoidia and the SAR202 clade (Figure 11 & 12, Appendix A Table 5 & 6). These results are supported by previous studies that have identified members of SAR202 cluster belonging to the Chloroflexi phylum to play a major role in the sulfur cycle in the dark water column. Some of these members have pathways for sulfur reduction and could be responsible for the hydrogen sulfide concentrations at depth. As previously stated, they also have the potential to metabolize a variety of organosulfur compounds [19]. In addition, single-cell genomics studies of members of the Dehalococcoidia class within the Chloroflexi phylum highlighted their association with marine sediments and sulfur cycling [13].
Differences in bioinformatic pipelines for microbial ecology data analysis may result in potential differences in analytical outcomes, and can lead to misidentifying species in different habitats or incorrectly determining trends. As we observed in this study, although mothur and QIIME2 often produced similar patterns, they did not agree on details. While QIIME2 led us to conclude that there was a presence of four classes (plus one uncultured class) of Chloroflexi in our samples, mothur only identified two. These differences are concerning, since depending on the pipeline we use, we might miss organisms we have collected, or we might identify organisms that are in reality not there. Consequently, we can be drawing the wrong conclusions about ecosystems, and the interplay of its inhabitants. Furthermore, whether we find significant correlations can also depend on the pipeline used. While Chloroflexi abundance was found to significantly correlate with depth and oxygen concentration for QIIME2, the correlation was not significant for mothur. In fact, the significances differed by an order of magnitude. This emphasizes the concern that significance in analysis findings may rest upon the usage of different pipelines and clustering paradigms, leading to false-positives and false-negatives. This also highlights the importance of developing objective metrics to gauge the accuracy of both clustering paradigms.
Differences we observed between the usage of mothur versus QIIME2 to analyse the reads in this study can also be seen in other research. For instance, a study examining the composition of chicken cecum microbiome performed by Allalil et al. revealed lower phylogenetic diversity (PD) values when UPARSE pipelines were used in comparison to de novo QIIME pipelines and open reference QIIME pipelines [25]. However, Species Richness (S) values were comparable when comparing different pipelines. In addition, the number of assigned sequences for different sequencing platform runs were impacted because of OTU picking using different pipelines: De novo vs. open reference QIIME pipelines. The QIIME pipelines generated different relative abundance of specific genera in comparison to UPARSE. Moreover, differences in the detection profiles, such as the number of unique species, were observed when using different pipelines. The number of OTUs and taxonomic assignments produced and identified differed between pipelines with a 99% similarity threshold.
Since multiple classes were identified under the order Chloroflexi and variations in the directions of correlation with depth were observed for several clusters, future analyses may find more meaning at a sub-phylum level. Additionally, more data were obtained than were analyzed in the current report. In the future, one could look for correlations of abundance across the other factors, such as temperature or salinity, not just depth, oxygen concentration, and hydrogen sulfide. Furthermore, there is a gap in the data between the depth of 10m and depth of 100m, which makes it difficult to determine correlations. More consistent data collection with more samples and at more regular intervals could help alleviate such problems, and potentially show significant correlations. Analysis of data available from the collection of samples over time could be interesting in exploring how the diversity in the area changes over seasons, or over longer time periods, such as decades. It could also be of interest, for any unknown, or not very well known, organisms to look into more details of their genetic make up in order to determine what roles, if any, they might play in biogeochemical cycles.
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Appendices
Appendix A: Correlation of Chloroflexi OTUs/ASVs Abundance with Depth, Oxygen Concentration, and Hydrogen Sulfide Concentration
| Class | Genus | Estimate | Std. Error | t value | Pr(>|t|) (p-value) | |
|---|---|---|---|---|---|---|
| Otu0181 | SAR202_clade | SAR202_clade_ge | -0.1713584 | 0.3794814 | -0.4515595 | 0.6704985 |
| Otu1579 | SAR202_clade | SAR202_clade_ge | -0.0073322 | 0.0162544 | -0.4510917 | 0.6708136 |
| Otu1149 | SAR202_clade | SAR202_clade_ge | -0.0035352 | 0.0082586 | -0.4280621 | 0.6864177 |
| Otu4286 | SAR202_clade | SAR202_clade_ge | -0.0035352 | 0.0082586 | -0.4280621 | 0.6864177 |
| Otu1064 | SAR202_clade | SAR202_clade_ge | -0.0027496 | 0.0165362 | -0.1662767 | 0.8744539 |
| Otu2632 | SAR202_clade | SAR202_clade_ge | -0.0023568 | 0.0055057 | -0.4280621 | 0.6864177 |
| Otu4287 | SAR202_clade | SAR202_clade_ge | -0.0011784 | 0.0027529 | -0.4280621 | 0.6864177 |
| Otu2381 | Anaerolineae | uncultured | -0.0005237 | 0.0056008 | -0.0935100 | 0.9291298 |
| Otu2592 | SAR202_clade | SAR202_clade_ge | -0.0005237 | 0.0056008 | -0.0935100 | 0.9291298 |
| Otu2591 | SAR202_clade | SAR202_clade_ge | -0.0002619 | 0.0028004 | -0.0935100 | 0.9291298 |
| Otu1577 | SAR202_clade | SAR202_clade_ge | 0.0001637 | 0.0036177 | 0.0452401 | 0.9656672 |
| Otu3712 | Anaerolineae | uncultured | 0.0008511 | 0.0055928 | 0.1521723 | 0.8850009 |
| Otu3607 | Anaerolineae | uncultured | 0.0034043 | 0.0023533 | 1.4465667 | 0.2076595 |
| Otu2790 | Anaerolineae | Thermomarinilinea | 0.0034043 | 0.0023533 | 1.4465667 | 0.2076595 |
| Otu3623 | Anaerolineae | Thermomarinilinea | 0.0036007 | 0.0053694 | 0.6705821 | 0.5322101 |
| Otu4340 | Anaerolineae | Anaerolineaceae_unclassified | 0.0068085 | 0.0047067 | 1.4465667 | 0.2076595 |
| Otu2789 | Anaerolineae | Thermomarinilinea | 0.0068085 | 0.0047067 | 1.4465667 | 0.2076595 |
| Otu1558 | Anaerolineae | Anaerolineaceae_unclassified | 0.0070049 | 0.0049223 | 1.4231039 | 0.2139907 |
| Otu1863 | Anaerolineae | Pelolinea | 0.0079214 | 0.0046360 | 1.7086714 | 0.1482107 |
| Otu3589 | Anaerolineae | uncultured | 0.0136170 | 0.0094133 | 1.4465667 | 0.2076595 |
| Otu1419 | Anaerolineae | Thermomarinilinea | 0.0136170 | 0.0094133 | 1.4465667 | 0.2076595 |
| Otu2497 | Anaerolineae | Pelolinea | 0.0136170 | 0.0094133 | 1.4465667 | 0.2076595 |
| Otu1147 | Anaerolineae | Thermomarinilinea | 0.0146645 | 0.0110438 | 1.3278449 | 0.2416173 |
| Otu1983 | Anaerolineae | Thermomarinilinea | 0.0158101 | 0.0123144 | 1.2838745 | 0.2554599 |
| Otu1246 | Anaerolineae | Thermomarinilinea | 0.0158429 | 0.0092720 | 1.7086714 | 0.1482107 |
| Otu1851 | Anaerolineae | Anaerolineaceae_unclassified | 0.0170213 | 0.0117667 | 1.4465667 | 0.2076595 |
| Otu0662 | Anaerolineae | Thermomarinilinea | 0.0340426 | 0.0235333 | 1.4465667 | 0.2076595 |
| Otu0551 | Anaerolineae | Thermomarinilinea | 0.0365957 | 0.0465283 | 0.7865264 | 0.4671821 |
| Otu1028 | Anaerolineae | Thermomarinilinea | 0.0374468 | 0.0258867 | 1.4465667 | 0.2076595 |
| Otu0607 | Anaerolineae | Thermomarinilinea | 0.0389853 | 0.0438394 | 0.8892756 | 0.4145865 |
| Otu0799 | Anaerolineae | Pelolinea | 0.0477578 | 0.0280660 | 1.7016226 | 0.1495636 |
| Otu0217 | Anaerolineae | Anaerolineaceae_unclassified | 0.1946645 | 0.2102439 | 0.9258985 | 0.3969899 |
| Otu0215 | Anaerolineae | Thermomarinilinea | 0.4527660 | 0.3129935 | 1.4465667 | 0.2076595 |
| Otu0195 | Anaerolineae | Anaerolineaceae_unclassified | 0.5344681 | 0.3694735 | 1.4465667 | 0.2076595 |
| Class | Genus | Estimate | Std. Error | t value | Pr(>|t|) (p-value) | |
|---|---|---|---|---|---|---|
| Asv1886 | D_2__SAR202 clade | D_5__ | -0.3397709 | 0.2301978 | -1.4759954 | 0.1999714 |
| Asv800 | D_2__SAR202 clade | D_5__ | -0.1327332 | 0.3683890 | -0.3603073 | 0.7333378 |
| Asv1266 | D_2__SAR202 clade | D_5__ | -0.0329951 | 0.0770801 | -0.4280621 | 0.6864177 |
| Asv1289 | D_2__Anaerolineae | D_5__uncultured | -0.0164975 | 0.0385401 | -0.4280621 | 0.6864177 |
| Asv1979 | D_2__Anaerolineae | D_5__uncultured | -0.0057610 | 0.0616089 | -0.0935100 | 0.9291298 |
| Asv1144 | D_2__SAR202 clade | D_5__ | -0.0039280 | 0.1354082 | -0.0290085 | 0.9779801 |
| Asv341 | D_2__JG30-KF-CM66 | D_5__ | -0.0035352 | 0.0082586 | -0.4280621 | 0.6864177 |
| Asv1862 | D_2__Anaerolineae | D_5__uncultured | -0.0034043 | 0.0364052 | -0.0935100 | 0.9291298 |
| Asv1260 | D_2__Anaerolineae | D_5__uncultured | -0.0007856 | 0.0084012 | -0.0935100 | 0.9291298 |
| Asv2081 | D_2__Anaerolineae | D_5__uncultured | 0.0011129 | 0.0027583 | 0.4034830 | 0.7032691 |
| Asv2034 | D_2__SAR202 clade | D_5__ | 0.0038298 | 0.0251674 | 0.1521723 | 0.8850009 |
| Asv1142 | D_2__JG30-KF-CM66 | D_5__ | 0.0057610 | 0.0801057 | 0.0719180 | 0.9454553 |
| Asv1046 | D_2__Anaerolineae | D_5__uncultured | 0.0111293 | 0.0275831 | 0.4034830 | 0.7032691 |
| Asv2247 | D_2__JG30-KF-CM66 | D_5__ | 0.0126023 | 0.0187931 | 0.6705821 | 0.5322101 |
| Asv400 | D_2__uncultured | D_5__ | 0.0136170 | 0.0094133 | 1.4465667 | 0.2076595 |
| Asv496 | D_2__Dehalococcoidia | NA | 0.0136170 | 0.0094133 | 1.4465667 | 0.2076595 |
| Asv2063 | D_2__Anaerolineae | NA | 0.0189198 | 0.0468912 | 0.4034830 | 0.7032691 |
| Asv134 | D_2__Anaerolineae | NA | 0.0234043 | 0.0349014 | 0.6705821 | 0.5322101 |
| Asv1473 | D_2__SAR202 clade | NA | 0.0238298 | 0.0164733 | 1.4465667 | 0.2076595 |
| Asv1794 | D_2__Anaerolineae | D_5__uncultured | 0.0238298 | 0.0164733 | 1.4465667 | 0.2076595 |
| Asv1234 | D_2__SAR202 clade | D_5__ | 0.0272340 | 0.0188267 | 1.4465667 | 0.2076595 |
| Asv477 | D_2__Anaerolineae | D_5__uncultured | 0.0288052 | 0.0429556 | 0.6705821 | 0.5322101 |
| Asv590 | D_2__Anaerolineae | D_5__uncultured | 0.0306383 | 0.0211800 | 1.4465667 | 0.2076595 |
| Asv1003 | D_2__SAR202 clade | D_5__ | 0.0306383 | 0.0211800 | 1.4465667 | 0.2076595 |
| Asv1282 | D_2__Anaerolineae | D_5__uncultured | 0.0340426 | 0.0235333 | 1.4465667 | 0.2076595 |
| Asv490 | D_2__Anaerolineae | D_5__uncultured | 0.0396072 | 0.0859823 | 0.4606434 | 0.6643958 |
| Asv1664 | D_2__Anaerolineae | D_5__uncultured | 0.0414075 | 0.0617486 | 0.6705821 | 0.5322101 |
| Asv1939 | D_2__Anaerolineae | D_5__Longilinea | 0.0418331 | 0.0911012 | 0.4591934 | 0.6653680 |
| Asv1163 | D_2__Anaerolineae | D_5__uncultured | 0.0476596 | 0.0329467 | 1.4465667 | 0.2076595 |
| Asv473 | D_2__Anaerolineae | D_5__uncultured | 0.0522095 | 0.0778570 | 0.6705821 | 0.5322101 |
| Asv2315 | D_2__Anaerolineae | D_5__uncultured | 0.0578723 | 0.0400067 | 1.4465667 | 0.2076595 |
| Asv1693 | D_2__Anaerolineae | D_5__uncultured | 0.0583633 | 0.0676342 | 0.8629259 | 0.4276204 |
| Asv555 | D_2__Anaerolineae | D_5__uncultured | 0.0748936 | 0.0517734 | 1.4465667 | 0.2076595 |
| Asv1943 | D_2__Anaerolineae | D_5__uncultured | 0.0792144 | 0.1181278 | 0.6705821 | 0.5322101 |
| Asv428 | D_2__Anaerolineae | D_5__uncultured | 0.0955810 | 0.1216822 | 0.7854968 | 0.4677332 |
| Asv114 | D_2__JG30-KF-CM66 | D_5__ | 0.1054664 | 0.1601592 | 0.6585100 | 0.5393201 |
| Asv2324 | D_2__Anaerolineae | NA | 0.1089362 | 0.0753067 | 1.4465667 | 0.2076595 |
| Asv1423 | D_2__Anaerolineae | D_5__Longilinea | 0.1123404 | 0.0776600 | 1.4465667 | 0.2076595 |
| Asv1505 | D_2__Anaerolineae | D_5__uncultured | 0.1123404 | 0.0776600 | 1.4465667 | 0.2076595 |
| Asv271 | D_2__Anaerolineae | D_5__uncultured | 0.1361702 | 0.0941334 | 1.4465667 | 0.2076595 |
| Asv208 | D_2__Anaerolineae | D_5__uncultured | 0.1468412 | 0.1522869 | 0.9642409 | 0.3792105 |
| Asv1095 | D_2__Anaerolineae | NA | 0.1634043 | 0.1129601 | 1.4465667 | 0.2076595 |
| Asv161 | D_2__Anaerolineae | D_5__uncultured | 0.1668085 | 0.1153134 | 1.4465667 | 0.2076595 |
| Asv1108 | D_2__Anaerolineae | D_5__uncultured | 0.1669722 | 0.1917355 | 0.8708462 | 0.4236697 |
| Asv408 | D_2__Anaerolineae | D_5__uncultured | 0.1859247 | 0.2109964 | 0.8811750 | 0.4185601 |
| Asv1071 | D_2__Anaerolineae | D_5__uncultured | 0.3438298 | 0.2376868 | 1.4465667 | 0.2076595 |
| Asv1749 | D_2__Anaerolineae | D_5__uncultured | 0.5208511 | 0.3600602 | 1.4465667 | 0.2076595 |
| Class | Genus | Estimate | Std. Error | t value | Pr(>|t|) (p-value) | |
|---|---|---|---|---|---|---|
| Otu0195 | Anaerolineae | Anaerolineaceae_unclassified | -0.1897296 | 0.3302310 | -0.5745358 | 0.5904867 |
| Otu0217 | Anaerolineae | Anaerolineaceae_unclassified | -0.1736086 | 0.1582994 | -1.0967102 | 0.3227568 |
| Otu0215 | Anaerolineae | Thermomarinilinea | -0.1607263 | 0.2797499 | -0.5745358 | 0.5904867 |
| Otu0181 | SAR202_clade | SAR202_clade_ge | -0.0520091 | 0.2990620 | -0.1739074 | 0.8687601 |
| Otu0607 | Anaerolineae | Thermomarinilinea | -0.0317385 | 0.0336870 | -0.9421584 | 0.3893700 |
| Otu0551 | Anaerolineae | Thermomarinilinea | -0.0269047 | 0.0362727 | -0.7417334 | 0.4915977 |
| Otu0799 | Anaerolineae | Pelolinea | -0.0200649 | 0.0258114 | -0.7773675 | 0.4721013 |
| Otu1028 | Anaerolineae | Thermomarinilinea | -0.0132932 | 0.0231372 | -0.5745358 | 0.5904867 |
| Otu0662 | Anaerolineae | Thermomarinilinea | -0.0120847 | 0.0210338 | -0.5745358 | 0.5904867 |
| Otu1983 | Anaerolineae | Thermomarinilinea | -0.0084593 | 0.0103315 | -0.8187858 | 0.4501563 |
| Otu1147 | Anaerolineae | Thermomarinilinea | -0.0084593 | 0.0092049 | -0.9189965 | 0.4002601 |
| Otu1246 | Anaerolineae | Thermomarinilinea | -0.0072508 | 0.0084400 | -0.8590967 | 0.4295406 |
| Otu1851 | Anaerolineae | Anaerolineaceae_unclassified | -0.0060423 | 0.0105169 | -0.5745358 | 0.5904867 |
| Otu1419 | Anaerolineae | Thermomarinilinea | -0.0048339 | 0.0084135 | -0.5745358 | 0.5904867 |
| Otu2497 | Anaerolineae | Pelolinea | -0.0048339 | 0.0084135 | -0.5745358 | 0.5904867 |
| Otu3589 | Anaerolineae | uncultured | -0.0048339 | 0.0084135 | -0.5745358 | 0.5904867 |
| Otu1558 | Anaerolineae | Anaerolineaceae_unclassified | -0.0036254 | 0.0042200 | -0.8590967 | 0.4295406 |
| Otu1863 | Anaerolineae | Pelolinea | -0.0036254 | 0.0042200 | -0.8590967 | 0.4295406 |
| Otu2789 | Anaerolineae | Thermomarinilinea | -0.0024169 | 0.0042068 | -0.5745358 | 0.5904867 |
| Otu4340 | Anaerolineae | Anaerolineaceae_unclassified | -0.0024169 | 0.0042068 | -0.5745358 | 0.5904867 |
| Otu3623 | Anaerolineae | Thermomarinilinea | -0.0024169 | 0.0042068 | -0.5745358 | 0.5904867 |
| Otu1064 | SAR202_clade | SAR202_clade_ge | -0.0020728 | 0.0128145 | -0.1617557 | 0.8778315 |
| Otu1579 | SAR202_clade | SAR202_clade_ge | -0.0012945 | 0.0128349 | -0.1008584 | 0.9235824 |
| Otu3712 | Anaerolineae | uncultured | -0.0012919 | 0.0043048 | -0.3001126 | 0.7761680 |
| Otu2790 | Anaerolineae | Thermomarinilinea | -0.0012085 | 0.0021034 | -0.5745358 | 0.5904867 |
| Otu3607 | Anaerolineae | uncultured | -0.0012085 | 0.0021034 | -0.5745358 | 0.5904867 |
| Otu1577 | SAR202_clade | SAR202_clade_ge | -0.0009643 | 0.0027703 | -0.3480925 | 0.7419469 |
| Otu2592 | SAR202_clade | SAR202_clade_ge | -0.0006367 | 0.0043341 | -0.1469078 | 0.8889445 |
| Otu2381 | Anaerolineae | uncultured | -0.0006367 | 0.0043341 | -0.1469078 | 0.8889445 |
| Otu1149 | SAR202_clade | SAR202_clade_ge | -0.0004881 | 0.0065115 | -0.0749568 | 0.9431557 |
| Otu4286 | SAR202_clade | SAR202_clade_ge | -0.0004881 | 0.0065115 | -0.0749568 | 0.9431557 |
| Otu2632 | SAR202_clade | SAR202_clade_ge | -0.0003254 | 0.0043410 | -0.0749568 | 0.9431557 |
| Otu2591 | SAR202_clade | SAR202_clade_ge | -0.0003184 | 0.0021670 | -0.1469078 | 0.8889445 |
| Otu4287 | SAR202_clade | SAR202_clade_ge | -0.0001627 | 0.0021705 | -0.0749568 | 0.9431557 |
| Class | Genus | Estimate | Std. Error | t value | Pr(>|t|) (p-value) | |
|---|---|---|---|---|---|---|
| Asv1749 | D_2__Anaerolineae | D_5__uncultured | -0.1848957 | 0.3218175 | -0.5745358 | 0.5904867 |
| Asv408 | D_2__Anaerolineae | D_5__uncultured | -0.1764060 | 0.1570152 | -1.1234959 | 0.3122557 |
| Asv1108 | D_2__Anaerolineae | D_5__uncultured | -0.1646951 | 0.1413959 | -1.1647802 | 0.2966535 |
| Asv208 | D_2__Anaerolineae | D_5__uncultured | -0.1439410 | 0.1112113 | -1.2943021 | 0.2521126 |
| Asv1071 | D_2__Anaerolineae | D_5__uncultured | -0.1220553 | 0.2124416 | -0.5745358 | 0.5904867 |
| Asv428 | D_2__Anaerolineae | D_5__uncultured | -0.1061416 | 0.0879361 | -1.2070308 | 0.2814027 |
| Asv114 | D_2__JG30-KF-CM66 | D_5__ | -0.0969221 | 0.1218861 | -0.7951862 | 0.4625657 |
| Asv800 | D_2__SAR202 clade | D_5__ | -0.0875482 | 0.2864534 | -0.3056281 | 0.7722028 |
| Asv161 | D_2__Anaerolineae | D_5__uncultured | -0.0592150 | 0.1030657 | -0.5745358 | 0.5904867 |
| Asv1095 | D_2__Anaerolineae | NA | -0.0580065 | 0.1009624 | -0.5745358 | 0.5904867 |
| Asv1943 | D_2__Anaerolineae | D_5__uncultured | -0.0531726 | 0.0925488 | -0.5745358 | 0.5904867 |
| Asv271 | D_2__Anaerolineae | D_5__uncultured | -0.0483387 | 0.0841353 | -0.5745358 | 0.5904867 |
| Asv1939 | D_2__Anaerolineae | D_5__Longilinea | -0.0476310 | 0.0688397 | -0.6919125 | 0.5198017 |
| Asv490 | D_2__Anaerolineae | D_5__uncultured | -0.0452141 | 0.0649448 | -0.6961928 | 0.5173357 |
| Asv1693 | D_2__Anaerolineae | D_5__uncultured | -0.0447133 | 0.0524914 | -0.8518223 | 0.4332067 |
| Asv1505 | D_2__Anaerolineae | D_5__uncultured | -0.0398795 | 0.0694116 | -0.5745358 | 0.5904867 |
| Asv1423 | D_2__Anaerolineae | D_5__Longilinea | -0.0398795 | 0.0694116 | -0.5745358 | 0.5904867 |
| Asv2324 | D_2__Anaerolineae | NA | -0.0386710 | 0.0673082 | -0.5745358 | 0.5904867 |
| Asv473 | D_2__Anaerolineae | D_5__uncultured | -0.0350456 | 0.0609981 | -0.5745358 | 0.5904867 |
| Asv1664 | D_2__Anaerolineae | D_5__uncultured | -0.0277948 | 0.0483778 | -0.5745358 | 0.5904867 |
| Asv555 | D_2__Anaerolineae | D_5__uncultured | -0.0265863 | 0.0462744 | -0.5745358 | 0.5904867 |
| Asv1144 | D_2__SAR202 clade | D_5__ | -0.0252671 | 0.1043155 | -0.2422180 | 0.8182327 |
| Asv2063 | D_2__Anaerolineae | NA | -0.0205440 | 0.0357575 | -0.5745358 | 0.5904867 |
| Asv2315 | D_2__Anaerolineae | D_5__uncultured | -0.0205440 | 0.0357575 | -0.5745358 | 0.5904867 |
| Asv477 | D_2__Anaerolineae | D_5__uncultured | -0.0193355 | 0.0336541 | -0.5745358 | 0.5904867 |
| Asv1163 | D_2__Anaerolineae | D_5__uncultured | -0.0169186 | 0.0294474 | -0.5745358 | 0.5904867 |
| Asv134 | D_2__Anaerolineae | NA | -0.0157101 | 0.0273440 | -0.5745358 | 0.5904867 |
| Asv1282 | D_2__Anaerolineae | D_5__uncultured | -0.0120847 | 0.0210338 | -0.5745358 | 0.5904867 |
| Asv1046 | D_2__Anaerolineae | D_5__uncultured | -0.0120847 | 0.0210338 | -0.5745358 | 0.5904867 |
| Asv1142 | D_2__JG30-KF-CM66 | D_5__ | -0.0112835 | 0.0618942 | -0.1823031 | 0.8625058 |
| Asv590 | D_2__Anaerolineae | D_5__uncultured | -0.0108762 | 0.0189304 | -0.5745358 | 0.5904867 |
| Asv1003 | D_2__SAR202 clade | D_5__ | -0.0108762 | 0.0189304 | -0.5745358 | 0.5904867 |
| Asv1234 | D_2__SAR202 clade | D_5__ | -0.0096677 | 0.0168271 | -0.5745358 | 0.5904867 |
| Asv1794 | D_2__Anaerolineae | D_5__uncultured | -0.0084593 | 0.0147237 | -0.5745358 | 0.5904867 |
| Asv1473 | D_2__SAR202 clade | NA | -0.0084593 | 0.0147237 | -0.5745358 | 0.5904867 |
| Asv2247 | D_2__JG30-KF-CM66 | D_5__ | -0.0084593 | 0.0147237 | -0.5745358 | 0.5904867 |
| Asv1979 | D_2__Anaerolineae | D_5__uncultured | -0.0070038 | 0.0476747 | -0.1469078 | 0.8889445 |
| Asv2034 | D_2__SAR202 clade | D_5__ | -0.0058137 | 0.0193716 | -0.3001126 | 0.7761680 |
| Asv496 | D_2__Dehalococcoidia | NA | -0.0048339 | 0.0084135 | -0.5745358 | 0.5904867 |
| Asv400 | D_2__uncultured | D_5__ | -0.0048339 | 0.0084135 | -0.5745358 | 0.5904867 |
| Asv1266 | D_2__SAR202 clade | D_5__ | -0.0045554 | 0.0607736 | -0.0749568 | 0.9431557 |
| Asv1862 | D_2__Anaerolineae | D_5__uncultured | -0.0041386 | 0.0281714 | -0.1469078 | 0.8889445 |
| Asv1289 | D_2__Anaerolineae | D_5__uncultured | -0.0022777 | 0.0303868 | -0.0749568 | 0.9431557 |
| Asv2081 | D_2__Anaerolineae | D_5__uncultured | -0.0012085 | 0.0021034 | -0.5745358 | 0.5904867 |
| Asv1260 | D_2__Anaerolineae | D_5__uncultured | -0.0009551 | 0.0065011 | -0.1469078 | 0.8889445 |
| Asv341 | D_2__JG30-KF-CM66 | D_5__ | -0.0004881 | 0.0065115 | -0.0749568 | 0.9431557 |
| Asv1886 | D_2__SAR202 clade | D_5__ | 0.1614351 | 0.2011515 | 0.8025547 | 0.4586642 |
| Class | Genus | Estimate | Std. Error | t value | Pr(>|t|) (p-value) | FDR Adjusted p-value | |
|---|---|---|---|---|---|---|---|
| Otu0181 | SAR202_clade | SAR202_clade_ge | -2.4575728 | 3.3035421 | -0.7439205 | 0.4903847 | 0.7285229 |
| Otu0217 | Anaerolineae | Anaerolineaceae_unclassified | -0.9242423 | 2.0042270 | -0.4611465 | 0.6640586 | 0.7285229 |
| Otu0607 | Anaerolineae | Thermomarinilinea | -0.1124721 | 0.4212890 | -0.2669714 | 0.8001524 | 0.8501620 |
| Otu1064 | SAR202_clade | SAR202_clade_ge | -0.0796436 | 0.1448049 | -0.5500061 | 0.6059809 | 0.7285229 |
| Otu1579 | SAR202_clade | SAR202_clade_ge | -0.0796436 | 0.1448049 | -0.5500061 | 0.6059809 | 0.7285229 |
| Otu1149 | SAR202_clade | SAR202_clade_ge | -0.0341330 | 0.0740614 | -0.4608737 | 0.6642415 | 0.7285229 |
| Otu4286 | SAR202_clade | SAR202_clade_ge | -0.0341330 | 0.0740614 | -0.4608737 | 0.6642415 | 0.7285229 |
| Otu0551 | Anaerolineae | Thermomarinilinea | -0.0280166 | 0.4433827 | -0.0631883 | 0.9520649 | 0.9520649 |
| Otu2381 | Anaerolineae | uncultured | -0.0227553 | 0.0493743 | -0.4608737 | 0.6642415 | 0.7285229 |
| Otu2592 | SAR202_clade | SAR202_clade_ge | -0.0227553 | 0.0493743 | -0.4608737 | 0.6642415 | 0.7285229 |
| Otu2632 | SAR202_clade | SAR202_clade_ge | -0.0227553 | 0.0493743 | -0.4608737 | 0.6642415 | 0.7285229 |
| Otu3712 | Anaerolineae | uncultured | -0.0227553 | 0.0493743 | -0.4608737 | 0.6642415 | 0.7285229 |
| Otu1577 | SAR202_clade | SAR202_clade_ge | -0.0227553 | 0.0309087 | -0.7362104 | 0.4946701 | 0.7285229 |
| Otu2591 | SAR202_clade | SAR202_clade_ge | -0.0113777 | 0.0246871 | -0.4608737 | 0.6642415 | 0.7285229 |
| Otu4287 | SAR202_clade | SAR202_clade_ge | -0.0113777 | 0.0246871 | -0.4608737 | 0.6642415 | 0.7285229 |
| Otu3623 | Anaerolineae | Thermomarinilinea | 0.0032080 | 0.0503917 | 0.0636606 | 0.9517072 | 0.9520649 |
| Otu3607 | Anaerolineae | uncultured | 0.0552843 | 0.0049066 | 11.2673382 | 0.0000962 | 0.0002726 |
| Otu2790 | Anaerolineae | Thermomarinilinea | 0.0552843 | 0.0049066 | 11.2673382 | 0.0000962 | 0.0002726 |
| Otu1558 | Anaerolineae | Anaerolineaceae_unclassified | 0.0584922 | 0.0454851 | 1.2859653 | 0.2547855 | 0.5414192 |
| Otu1863 | Anaerolineae | Pelolinea | 0.0991909 | 0.0280249 | 3.5393861 | 0.0165734 | 0.0375663 |
| Otu2789 | Anaerolineae | Thermomarinilinea | 0.1105686 | 0.0098132 | 11.2673382 | 0.0000962 | 0.0002726 |
| Otu4340 | Anaerolineae | Anaerolineaceae_unclassified | 0.1105686 | 0.0098132 | 11.2673382 | 0.0000962 | 0.0002726 |
| Otu1983 | Anaerolineae | Thermomarinilinea | 0.1185885 | 0.1161660 | 1.0208533 | 0.3541507 | 0.6689512 |
| Otu1147 | Anaerolineae | Thermomarinilinea | 0.1203422 | 0.1022047 | 1.1774631 | 0.2920002 | 0.5840003 |
| Otu1246 | Anaerolineae | Thermomarinilinea | 0.1983818 | 0.0560498 | 3.5393861 | 0.0165734 | 0.0375663 |
| Otu3589 | Anaerolineae | uncultured | 0.2211371 | 0.0196264 | 11.2673382 | 0.0000962 | 0.0002726 |
| Otu1419 | Anaerolineae | Thermomarinilinea | 0.2211371 | 0.0196264 | 11.2673382 | 0.0000962 | 0.0002726 |
| Otu2497 | Anaerolineae | Pelolinea | 0.2211371 | 0.0196264 | 11.2673382 | 0.0000962 | 0.0002726 |
| Otu1851 | Anaerolineae | Anaerolineaceae_unclassified | 0.2764214 | 0.0245330 | 11.2673382 | 0.0000962 | 0.0002726 |
| Otu0662 | Anaerolineae | Thermomarinilinea | 0.5528428 | 0.0490660 | 11.2673382 | 0.0000962 | 0.0002726 |
| Otu1028 | Anaerolineae | Thermomarinilinea | 0.6081271 | 0.0539726 | 11.2673382 | 0.0000962 | 0.0002726 |
| Otu0799 | Anaerolineae | Pelolinea | 0.6618074 | 0.1140109 | 5.8047714 | 0.0021396 | 0.0055959 |
| Otu0215 | Anaerolineae | Thermomarinilinea | 7.3528091 | 0.6525773 | 11.2673382 | 0.0000962 | 0.0002726 |
| Otu0195 | Anaerolineae | Anaerolineaceae_unclassified | 8.6796317 | 0.7703356 | 11.2673382 | 0.0000962 | 0.0002726 |
| Class | Genus | Estimate | Std. Error | t value | Pr(>|t|) (p-value) | FDR Adjusted p-value | |
|---|---|---|---|---|---|---|---|
| Asv800 | D_2__SAR202 clade | D_5__ | -2.9695672 | 3.0816822 | -0.9636189 | 0.3794937 | 0.8004961 |
| Asv1886 | D_2__SAR202 clade | D_5__ | -2.2758300 | 2.2620814 | -1.0060778 | 0.3605552 | 0.8004961 |
| Asv1108 | D_2__Anaerolineae | D_5__uncultured | -1.2574021 | 1.7629163 | -0.7132512 | 0.5075876 | 0.8004961 |
| Asv408 | D_2__Anaerolineae | D_5__uncultured | -1.1503411 | 1.9735619 | -0.5828756 | 0.5852742 | 0.8004961 |
| Asv208 | D_2__Anaerolineae | D_5__uncultured | -1.1127006 | 1.4059595 | -0.7914172 | 0.4645707 | 0.8004961 |
| Asv428 | D_2__Anaerolineae | D_5__uncultured | -1.0569670 | 1.0591512 | -0.9979378 | 0.3641245 | 0.8004961 |
| Asv1144 | D_2__SAR202 clade | D_5__ | -0.6485262 | 1.1827890 | -0.5483025 | 0.6070659 | 0.8004961 |
| Asv114 | D_2__JG30-KF-CM66 | D_5__ | -0.6042138 | 1.4769566 | -0.4090938 | 0.6994049 | 0.8218007 |
| Asv1939 | D_2__Anaerolineae | D_5__Longilinea | -0.5119943 | 0.8044173 | -0.6364785 | 0.5524571 | 0.8004961 |
| Asv490 | D_2__Anaerolineae | D_5__uncultured | -0.4892390 | 0.7585529 | -0.6449637 | 0.5473730 | 0.8004961 |
| Asv1142 | D_2__JG30-KF-CM66 | D_5__ | -0.3868402 | 0.6996938 | -0.5528707 | 0.6041591 | 0.8004961 |
| Asv1266 | D_2__SAR202 clade | D_5__ | -0.3185743 | 0.6912399 | -0.4608737 | 0.6642415 | 0.8004961 |
| Asv1979 | D_2__Anaerolineae | D_5__uncultured | -0.2503083 | 0.5431170 | -0.4608737 | 0.6642415 | 0.8004961 |
| Asv2063 | D_2__Anaerolineae | NA | -0.1934201 | 0.4196813 | -0.4608737 | 0.6642415 | 0.8004961 |
| Asv1289 | D_2__Anaerolineae | D_5__uncultured | -0.1592871 | 0.3456199 | -0.4608737 | 0.6642415 | 0.8004961 |
| Asv1862 | D_2__Anaerolineae | D_5__uncultured | -0.1479095 | 0.3209328 | -0.4608737 | 0.6642415 | 0.8004961 |
| Asv1046 | D_2__Anaerolineae | D_5__uncultured | -0.1137765 | 0.2468714 | -0.4608737 | 0.6642415 | 0.8004961 |
| Asv2034 | D_2__SAR202 clade | D_5__ | -0.1023989 | 0.2221842 | -0.4608737 | 0.6642415 | 0.8004961 |
| Asv1693 | D_2__Anaerolineae | D_5__uncultured | -0.0964323 | 0.6505274 | -0.1482371 | 0.8879484 | 0.9517072 |
| Asv1260 | D_2__Anaerolineae | D_5__uncultured | -0.0341330 | 0.0740614 | -0.4608737 | 0.6642415 | 0.8004961 |
| Asv341 | D_2__JG30-KF-CM66 | D_5__ | -0.0341330 | 0.0740614 | -0.4608737 | 0.6642415 | 0.8004961 |
| Asv2081 | D_2__Anaerolineae | D_5__uncultured | -0.0113777 | 0.0246871 | -0.4608737 | 0.6642415 | 0.8004961 |
| Asv2247 | D_2__JG30-KF-CM66 | D_5__ | 0.0112279 | 0.1763709 | 0.0636606 | 0.9517072 | 0.9517072 |
| Asv134 | D_2__Anaerolineae | NA | 0.0208518 | 0.3275459 | 0.0636606 | 0.9517072 | 0.9517072 |
| Asv477 | D_2__Anaerolineae | D_5__uncultured | 0.0256637 | 0.4031335 | 0.0636606 | 0.9517072 | 0.9517072 |
| Asv1664 | D_2__Anaerolineae | D_5__uncultured | 0.0368916 | 0.5795044 | 0.0636606 | 0.9517072 | 0.9517072 |
| Asv473 | D_2__Anaerolineae | D_5__uncultured | 0.0465155 | 0.7306794 | 0.0636606 | 0.9517072 | 0.9517072 |
| Asv1943 | D_2__Anaerolineae | D_5__uncultured | 0.0705752 | 1.1086170 | 0.0636606 | 0.9517072 | 0.9517072 |
| Asv400 | D_2__uncultured | D_5__ | 0.2211371 | 0.0196264 | 11.2673382 | 0.0000962 | 0.0002380 |
| Asv496 | D_2__Dehalococcoidia | NA | 0.2211371 | 0.0196264 | 11.2673382 | 0.0000962 | 0.0002380 |
| Asv1473 | D_2__SAR202 clade | NA | 0.3869900 | 0.0343462 | 11.2673382 | 0.0000962 | 0.0002380 |
| Asv1794 | D_2__Anaerolineae | D_5__uncultured | 0.3869900 | 0.0343462 | 11.2673382 | 0.0000962 | 0.0002380 |
| Asv1234 | D_2__SAR202 clade | D_5__ | 0.4422742 | 0.0392528 | 11.2673382 | 0.0000962 | 0.0002380 |
| Asv590 | D_2__Anaerolineae | D_5__uncultured | 0.4975585 | 0.0441594 | 11.2673382 | 0.0000962 | 0.0002380 |
| Asv1003 | D_2__SAR202 clade | D_5__ | 0.4975585 | 0.0441594 | 11.2673382 | 0.0000962 | 0.0002380 |
| Asv1282 | D_2__Anaerolineae | D_5__uncultured | 0.5528428 | 0.0490660 | 11.2673382 | 0.0000962 | 0.0002380 |
| Asv1163 | D_2__Anaerolineae | D_5__uncultured | 0.7739799 | 0.0686923 | 11.2673382 | 0.0000962 | 0.0002380 |
| Asv2315 | D_2__Anaerolineae | D_5__uncultured | 0.9398327 | 0.0834121 | 11.2673382 | 0.0000962 | 0.0002380 |
| Asv555 | D_2__Anaerolineae | D_5__uncultured | 1.2162541 | 0.1079451 | 11.2673382 | 0.0000962 | 0.0002380 |
| Asv2324 | D_2__Anaerolineae | NA | 1.7690969 | 0.1570111 | 11.2673382 | 0.0000962 | 0.0002380 |
| Asv1423 | D_2__Anaerolineae | D_5__Longilinea | 1.8243812 | 0.1619177 | 11.2673382 | 0.0000962 | 0.0002380 |
| Asv1505 | D_2__Anaerolineae | D_5__uncultured | 1.8243812 | 0.1619177 | 11.2673382 | 0.0000962 | 0.0002380 |
| Asv271 | D_2__Anaerolineae | D_5__uncultured | 2.2113711 | 0.1962638 | 11.2673382 | 0.0000962 | 0.0002380 |
| Asv1095 | D_2__Anaerolineae | NA | 2.6536454 | 0.2355166 | 11.2673382 | 0.0000962 | 0.0002380 |
| Asv161 | D_2__Anaerolineae | D_5__uncultured | 2.7089297 | 0.2404232 | 11.2673382 | 0.0000962 | 0.0002380 |
| Asv1071 | D_2__Anaerolineae | D_5__uncultured | 5.5837121 | 0.4955662 | 11.2673382 | 0.0000962 | 0.0002380 |
| Asv1749 | D_2__Anaerolineae | D_5__uncultured | 8.4584946 | 0.7507092 | 11.2673382 | 0.0000962 | 0.0002380 |
Project 2 - Group 6
Abstract
Analysis of DNA and RNA sequences obtained from Saanich Inlet using high-throughput sequencing has allowed us to examine the nitrogen cycle in this area. This study serves as a great model to examine microbial community responses to changes in the environment such as environmental oxygen levels. Although various genes encode enzymes that are crucial to the nitrogen cycle, our study focuses on the abundance of the norC gene, which encodes the nitric oxide reductase subunit C, at various depths. Using the TreeSAPP pipeline it was shown that at all but one depth, norC was more abundant at the genome level than at the expression level. Any classified families that contained norC in their genome were also found to express it, although the ratios between DNA and RNA abundance differed between the families. Gammaproteobacteria contributed the most to norC expression levels at all but one depth, and represented the class with the most norC at the genome level as well. A notable exception for expression levels were Epsilonproteobacteria, which were found to express the most norC at depth 200m. NorC was found to correlate significantly with only one nitrogen species, NH4+, with which it had a positive correlation. Relations with all the other nitrogen species were negative and insignificant. Although our study focused on one specific gene, the methods could be used to examine the abundance of other genes either involved in the nitrogen cycle or other biogeochemical processes, as well as to gain insight into how microbial communities respond to changes in their surrounding environment.
Introduction
Saanich Inlet is a seasonally anoxic fjord located between Vancouver Island and the Saanich Peninsula [1]. It is 24 km long and has a basin of up to 234 meters in depth [2]. It has a 75-meter sill which acts to protect the deeper waters [3]. Because of this sill and the constantly high input of organic material from freshwater discharge and primary production in surface waters, its conditions below 110 meters are anoxic [3]. Oxygen replenishment is dependent on the season, occurring mostly in the fall, which modifies the oxygen gradient and thereby the environmental conditions for the microbial community that inhabits the inlet [3]. Dissolved oxygen increases gradually from a minimum concentration at greater depths up to its peak concentration at the surface due to phytoplankton metabolism and atmospheric surface waters gas exchange [3]. Nitrate reduction by denitrifiers happens mostly in the deep water following oxygenation [3]. This results in a steep nitrate gradient when looking at the different depths within the fjord [3]. A study by Zaikova et al. found that microbial diversity was highest in the hypoxic transition area and that it decreases within the anoxic basin waters [1]. It is essential to study the roles of various microorganisms within Saanich Inlet in order to understand how they affect environmental conditions like greenhouse gases, methane, and denitrogenation on a larger scale in the world’s oceans [3].
Oxygen minimum zones (OMZ) are areas of the ocean typically occurring between depths of about 200 to 1000 meters where oxygen concentrations are at their lowest [4]. Global OMZs are normally found along the western boundaries of continents where upwelling brings nutrient rich waters from the deep ocean to surface waters. This influx of nutrients increases primary production and therefore the input of organic particle and respiration rates at depth [5]. OMZs are also found in coastal basins where restricted circulation decreases the mixing of deep and surface waters [5]. Saanich Inlet is a glacially carved fjord and is an example of a coastal basin in which the water circulation is decreased by an entrance sill. This in turn restricts oxygen rich water from entering the basin and creates a seasonally anoxic zone at depth [5].
A reaction performed by microbes through multispecies microbial interaction is the conversion of the inert elemental nitrogen gas (N2) to a usable form that can be used by plants for nucleic acid and protein synthesis [6]. The reductive and irreversible process of converting elemental N2 to NH4+ is catalyzed by nitrogenase - a conserved enzyme complex that is inhibited in the presence of oxygen [6]. NH4+ can be oxidized to nitrate (NO3−) in the presence of oxygen through a two-step process. The first step is the oxidation of ammonia (NH4+) to nitrite (NO2−) by a particular group of Bacteria or Archaea and the second step involves oxidation of NO2− to NO3− by a different group of nitrifying microbes [6]. Finally, opportunistic microbes use NO2− and NO3− as electron acceptors in the absence of oxygen for the oxidation of organic matter. This process ultimately leads to the formation of N2 and completion of the nitrogen cycle [6]. The latter process is called denitrification and consist of the dissimilatory reduction of NO2− and NO3− by denitrifying facultative anaerobes to nitric oxide (NO) and nitrous oxide (N2O). These two nitrogen species are classified as ionic nitrogen oxides and act as terminal electron acceptors in anaerobic conditions where they are reduced to dinitrogen (N2) [7]. Four functional enzymes are involved in the process of denitrification: nitrate, nitrite, nitric oxide, and nitrous oxide reductases that are encoded by nar, nir, nor, and nis gene clusters, respectively [8].
It is important to note that denitrification increases in oxygen minimum zones (OMZs) in the ocean. The lack of oxygen results in heterotrophic microbes using other oxidants including nitrate and nitrite. This leads to loss of nitrogen from the ocean in the form of N2 [9]. Denitrification is an undesirable process for soil fertility and agricultural productivity as it results in loss of fertilizer nitrogen (nitrate) from soil environments [7]. However, it plays a major role in removal of nitrogen from waste, such as animal residues. Denitrification is of major ecological importance since it is responsible for the supply of NO2− to the atmosphere and causes stratospheric reactions leading to the depletion of ozone [7]. The absence of this process would result in the accumulation of N2 in the atmosphere and NO3−, a toxin if found at high concentrations, in water and soil environments. In conclusion, the absence of denitrification would result in disruption of the nitrogen cycle [7].
The microbial species involved in denitrification are able to use nitrogen oxides as electron acceptors in place of oxygen under anaerobic conditions. One group of denitrifying bacteria is photosynthetic; however, most are heterotrophs and some are autotrophs that utilize reduced sulphur compounds or H2 and CO2 [7]. Important denitrifiers isolated from soil and aquatic environments include members of the Achromobacter, Alcaligenes, Bacillus, Chromobacterium, Chromobacterium, Halobacterium, Hyphomicribium, Moraxella, Paracoccus, and Pseudomonas genera [7]. Altogether, the nitrogen cycle could be more accurately represented as a complex metabolic network [10], and the Saanich Inlet is a model ecosystem for studying how the components of that network are distributed across both taxa and depth [3]. We focused our investigations on norC, which is the component of the denitrification pathway responsible for the reduction of NO to N2O.
Methods
Sample collection and processing
Water samples were collected from 16 depths (10-200m) at station S3 in Saanich Inlet (48°35.500 N, 123°30.300 W) onboard MSV John Strickland during cruise 72 (August 1, 2012). Water samples (2L) were filtered through a 0.22 µm Sterivex filter to collect biomass, which was stored at -80˚C for further multi-omic analyses. Geochemical data was measured in situ using a Sea-Bird SBE 43 and in the laboratory on water samples collected using various assays [3, 11].
Total genomic DNA and RNA was extracted from Sterivex filters for 7 depths (10, 100, 120, 135, 150, 165, 200m). Illumina metagenomic shotgun libraries were constructed from reversed transcribed genomic RNA (cDNA) and genomic DNA, and were paired end sequenced (2x150bp technology) on the Illumina HiSeq platform at the US Department of Energy Joint Genome Institute (DOE JGI). Processing and quality control of the output reads were also done at JGI using the IMG/M pipeline, and the assembly and processing of the metagenomes were done at the University of British Columbia using MetaPathways 2.5 [11].
In-depth sampling and sequencing methods can be found here: Hawley AK et al 2017, “A compendium of multi-omic sequence information from the Saanich Inlet water column” Sci Data 4: 170160.
Data analysis
TreeSAPP
Tree-based Sensitive and Accurate Protein Profiler (TreeSAPP) was used through Google Cloud services to reconstruct the nitrogen cycle in Saanich Inlet along defined redox gradients. Computational servers were linked to Google Cloud storage using the gcsfuse command. To allow for continuous processing of the data while offline the ‘nohup’ command was used. DNA and RNA assemblies were processed for norC (D0502) through the TreeSAPP pipeline and SAM files were immediately removed using the following code:
# Code for NorC processing of the RNA and DNA samples
rm -rf treesapp_out
for 10m, 100m, 120m, 135m, 150m, 165m and 200m depths:
time treesapp.py -T 8 --verbose --delete -t D0502 --rpkm \
-i bucket/MetaG_assemblies/SI072_LV_"$depth"_DNA.scaffolds.fasta \
-r bucket/MetaG_reads/SI072_LV_"$depth".anqdp.fastq.gz \
-o treesapp_out/dna/"$depth"
rm treesapp_out/dna/"$depth"/RPKM_outputs/*.sam
time treesapp.py -T 8 --verbose --delete -t D0502 --rpkm \
-i bucket/MetaG_assemblies/SI072_LV_"$depth"_DNA.scaffolds.fasta \
-r bucket/MetaT_reads/SI072_LV_"$depth".qtrim.3ptrim.artifact.rRNA.clean.fastq.gz \
-o treesapp_out/rna/"$depth"
rm treesapp_out/rna/"$depth"/RPKM_outputs/*.samiTOL
TreeSAPP output data found under treesapp_out/iTOL_output/ were visualized in a phylogenetic tree using Interactive Tree of Life (iTOL) software 4.2 v1.0 [12].
R setup
Analysis was completed in R v3.4.3 [13] using the following packages.
library(tidyverse)
library(cowplot)
library(phyloseq)
library(grid)
library(knitr)Taxonomy, abundance, and query data were loaded from “marker_contig_map.tsv” files obtained from TreeSAPP.
treesapp.out.dir = "./treesapp_out_gene_tax_abd/"
# Example data loading
norC.DNA.10m = read_tsv(paste(treesapp.out.dir, "dna/10m/final_outputs/marker_contig_map.tsv", sep="")) %>%
select(Tax.DNA.10 = Confident_Taxonomy, Abund.DNA.10 = Abundance, Query)The data were then combined into a single data frame.
# Combine the data frames
norC.all = norC.DNA.10m %>%
full_join(norC.DNA.100m, by = "Query") %>%
full_join(norC.DNA.120m, by = "Query") %>%
full_join(norC.DNA.135m, by = "Query") %>%
full_join(norC.DNA.150m, by = "Query") %>%
full_join(norC.DNA.165m, by = "Query") %>%
full_join(norC.DNA.200m, by = "Query") %>%
full_join(norC.RNA.10m, by = "Query") %>%
full_join(norC.RNA.100m, by = "Query") %>%
full_join(norC.RNA.120m, by = "Query") %>%
full_join(norC.RNA.135m, by = "Query") %>%
full_join(norC.RNA.150m, by = "Query") %>%
full_join(norC.RNA.165m, by = "Query") %>%
full_join(norC.RNA.200m, by = "Query") %>%
# Create a taxonomy variable aggregating all taxonomy columns to fill in any NAs
mutate(Taxonomy = ifelse(!is.na(Tax.DNA.10), Tax.DNA.10,
ifelse(!is.na(Tax.DNA.100), Tax.DNA.100,
ifelse(!is.na(Tax.DNA.120), Tax.DNA.120,
ifelse(!is.na(Tax.DNA.135), Tax.DNA.135,
ifelse(!is.na(Tax.DNA.150), Tax.DNA.150,
ifelse(!is.na(Tax.DNA.165), Tax.DNA.165,
ifelse(!is.na(Tax.DNA.200), Tax.DNA.200,
ifelse(!is.na(Tax.RNA.10), Tax.RNA.10,
ifelse(!is.na(Tax.RNA.100), Tax.RNA.100,
ifelse(!is.na(Tax.RNA.120), Tax.RNA.120,
ifelse(!is.na(Tax.RNA.135), Tax.RNA.135,
ifelse(!is.na(Tax.RNA.150), Tax.RNA.150,
ifelse(!is.na(Tax.RNA.165), Tax.RNA.165,
ifelse(!is.na(Tax.RNA.200), Tax.RNA.200,
"unclassified"))))))))))))))) %>%
# Remove old Tax variables
select(-starts_with("Tax.")) %>%
# Gather all the abundance data into 1 column
gather("Key", "Abundance", starts_with("Abund")) %>%
# Turn the Key into Depth and RNA/DNA variables
separate(Key, c("Key","Type","Depth_m"), by = ".") %>%
# Remove Key variable and reorder the columns with Query at the end
select(Depth_m, Type, Abundance, Taxonomy, Query) %>%
# Make depth numerical
mutate(Depth_m = as.numeric(Depth_m)) %>%
# Separate Taxonomy into columns
separate(Taxonomy, into = c("Domain", "Phylum", "Class", "Order", "Family", "Genus", "Species"), sep="; ")Note that not all queries could be classified at the species level, so those cells were filled in with NA.
Geochemical data from cruise 72 (Aug 1, 2012) were loaded in order to draw correlations with norC abundance.
load("mothur_phyloseq.RData")
metadata = data.frame(mothur@sam_data)Analysis steps
The variation in the abundance of norC DNA and RNA with depth was examined by plotting the abundances at the different depths. Both abundances were then stratified at the family and class level as well. The presence of norC RNA was then examined in relation to N2O, NH4+, NO2-, NO3-, and O2 at the different depths. The abundances were also correlated with nitrogen species N2O, NH4+, NO2-, NO3- and O2 using a linear model using the lm() function in the stats R packages as per code found in the results section [14].
Results
NorC DNA and RNA Abundance with Depth
# Save gene abundance data
gene_abd_depth = norC.all %>%
# Group data by depth and type (RNA/DNA)
group_by(Depth_m, Type) %>%
# Filter out NAs
filter(!is.na(Abundance)) %>%
# Sum up the abundance data for each depth for RNA and DNA
summarize(Abundance = sum(Abundance)) %>%
# Round the sums
mutate(Abundance = round(Abundance, digits = 2))
# Plot the abundance data
ggplot(gene_abd_depth, aes(x = Type, y = Depth_m)) +
geom_point(aes(size = Abundance)) +
geom_text(aes(label = Abundance, hjust = -0.5)) +
scale_y_reverse(lim=c(200,10)) +
labs(x = NULL) +
theme(legend.position="none")
Figure 1 RPKM abundance (value labels) of norC at the genome and expression level across different depths for samples obtained at Saanich Inlet.
The abundance of norC differs with depth (Figure 1). No norC was found at either the genome or expression level at 10m. For DNA the abundance is increasing with depth until 150m, where it is maximal. Then the abundance abruptly drops at 165m, and then is relatively high again at 200m. For RNA, norC abundance is relatively low up to depth 135m, only slowly increasing. Then the abundance is notably greater at 150m, and maximum RNA abundance is reached at 165m. Then RNA abundance decreases again at 200m.
The graph shows that the abundance of DNA differs from the abundance of RNA. DNA abundance is notably higher at depths up to depth 150m. DNA abundance is maximal at 150m, where RNA abundance is lower, and RNA abundance is maximal at 165m, where DNA abundance is quite low. We see about the same amount of norC RNA and DNA at 200m.
Taxa Responsible for norC Abundance
norC.all %>%
# Change NAs to "unclassified"
mutate(Family = ifelse(is.na(Family), "unclassified", Family)) %>%
# Remove 0 abundance
mutate(Abundance = ifelse(Abundance == 0, NA, Abundance)) %>%
# Plot the data
ggplot(aes(x = Family, y = Depth_m)) +
# Keep points from overlapping
geom_point(aes(size = Abundance, color = Type), position = position_dodge(0.6)) +
scale_y_reverse(lim=c(200,10)) +
labs(x = "Family") +
theme(axis.text.x = element_text(angle = 20, hjust = 1), plot.margin= margin(0,0,0,20))
Figure 2 RPKM abundance of norC stratified at the family level. Abundance is shown at both the genome and expression level across different depths. The size of each circle is proportional to abundance.
In total seven fully classified families were found to contribute to the norC DNA and RNA levels, along with some unclassified Thiotrichales and fully unclassified organisms (Figure 2). All classified families that contribute some amount to the DNA levels, were found to also contribute a nonzero amount to the RNA levels.
A different number of classified families was found to contribute to the DNA and RNA levels at different depths. The smallest number of classified families, only three, contribute at depths of 100m, 120m, and 165m, while the biggest number of classified families, six, contribute at a depth of 150m. Some families were found to contribute to norC levels only at one depth, while others contribute at more depths.
At every depth, the family Candidatus Thioglobus was found to contribute the most to norC DNA abundance, while the second biggest contributors were unclassified organisms. The biggest contributors to RNA abundance at the majority of depths were found to be unclassified organisms. This is while at depth 200m Helicobacteraceae contribute the most to RNA abundance.
norC.all %>%
# Change NAs to "unclassified"
mutate(Class = ifelse(is.na(Class), "unclassified", Class)) %>%
# Remove 0 abundance
mutate(Abundance = ifelse(Abundance == 0, NA, Abundance)) %>%
ggplot(aes(x = Class, y = Depth_m)) +
# Keep points from overlapping
geom_point(aes(size = Abundance, color = Type), position = position_dodge(0.6)) +
scale_y_reverse(lim=c(200,10)) +
labs(x = "Class") +
theme(axis.text.x = element_text(angle = 20, hjust = 1))
Figure 3 RPKM abundance of norC stratified at the class level. Abundance is shown at both the genome and expression level across different depths. The size of each circle is proportional to abundance.
Viewed at the class level, it can be seen that the unclassified organisms responsible for most of the RNA are of the class Gammaproteobacteria (Figure 3).
These patterns can also be seen by visualizing the data on iTOL (sample in Appendix Figure A1). The expression of norC is observed to be localized to a few classes. Classes with the highest levels of norC expression are variable with respect to depth. Epsilonproteobacteria and Ardenticatenia were observed to have greater expression at 135m or shallower, while Gammaproteobacteria expression is more biased towards greater depths.
NorC Abundance and Nitrogen Species
# Save plot of abundance data
plot_abd_depth = ggplot(gene_abd_depth, aes(x = Type, y = Depth_m)) +
geom_point(aes(size = Abundance)) +
scale_y_reverse(lim=c(200,10)) +
labs(x = "", y = NULL) +
theme(plot.margin = unit(c(0,0,0.55,0), "cm"))# Order the data by depth
metadata_depth = metadata %>% arrange(Depth_m)
# Example plot code
# Save plot for NO3 metadata as it changes with depth
plot_NO3 = metadata_depth %>%
ggplot(aes(x = NO3_uM, y = Depth_m)) +
geom_point() +
geom_path(aes(group = 1)) +
scale_y_reverse(lim=c(200,10)) +
labs(y = "Depth (m)", x = "NO3 (uM)") +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
plot.margin = unit(c(0,0.1,0.3,0), "cm"))# Create composite figure
plot_grid(plot_N2O, plot_NH4, plot_NO2, plot_NO3, plot_O2, plot_abd_depth,
labels=c("A", "B", "C", "D", "E", "F"),
nrow = 1,
rel_widths=c(1.2,1,1,1,1,2.4))
Figure 4 Nitrogen species and oxygen concentrations across depth (A-E) compared to norC abundance at both the genome and expression level (F; reproduced from Figure 1)
The relation between norC abundance and nitrogen species and oxygen was examined (Figure 4). NO3- and N2O concentrations are seen to peak at 100m, where some norC DNA is observed, but not much norC RNA. NO3- and N2O then decrease with greater depth, as DNA and RNA levels increase. NO2- is relatively constant, between about 0.05uM to 0.10uM, at depths 100m to 165m, where norC DNA and RNA levels increase. This is while NH4+ increases in concentration with depth, starting at a depth of 150m, seemingly increasing with RNA levels. There was lower oxygen concentration where there was a higher RNA abundance. These possible relationships were further examined with linear models.
# Combine gene abundance data with metadata
gene_abd_depth_meta = metadata %>%
# Change N2O to also be in uM like the others
mutate(N2O_uM = N2O_nM / 1000) %>%
# Select columns of interest
select(Depth_m, NH4_uM, N2O_uM, NO2_uM, NO3_uM, O2_uM) %>%
# Rename the columns
rename("NH4 (uM)" = NH4_uM) %>%
rename("N2O (uM)" = N2O_uM) %>%
rename("NO2 (uM)" = NO2_uM) %>%
rename("NO3 (uM)" = NO3_uM) %>%
rename("O2 (uM)" = O2_uM) %>%
# Join the abundance data and metadata
gather(Nutrient,uM,-Depth_m) %>%
full_join(gene_abd_depth, by = "Depth_m") %>%
unite(Type_Nutrient, Type, Nutrient, sep = " - ") %>%
arrange(desc(Type_Nutrient))
# Plot the data
ggplot(gene_abd_depth_meta, aes(x = uM, y=Abundance)) +
geom_point() +
facet_wrap(~Type_Nutrient, scales="free", ncol = 5) +
geom_smooth(method='lm') +
labs(y = "Abundance", x = "Concentration (uM)") +
theme(text = element_text(size=18))
Figure 5 RPKM abundance of norC at different nitrogen species concentrations. Abundance is shown at both the genome and expression level.
The presence and expression of the norC gene was only positively associated with NH4+ levels, while all other correlations revealed negative trends.
# Initialize a data frame
RNA_abundance = data.frame(matrix(ncol = 5, nrow= 0))
for (type_nutrient in unique(gene_abd_depth_meta$Type_Nutrient)) {
# Get the summary of lm()
summ = gene_abd_depth_meta %>%
filter(Type_Nutrient == type_nutrient) %>%
lm(Abundance ~ uM, .) %>%
summary()
# Pull out the statistics values
coef = summ$coefficients["uM",]
# Convert into a data frame
coef_data_frame = data.frame(coef=matrix(coef),row.names=names(coef))
# Add to data frame for table
RNA_abundance[nrow(RNA_abundance) + 1,] <-c(type_nutrient, round(coef_data_frame[,1], 3))
}
# Reverse order to agree with graphs
RNA_abundance = RNA_abundance[nrow(RNA_abundance):1,]
# Name the columns
colnames(RNA_abundance) <- (c("Correlation", "Estimate", "Std. Error","t value","Pr(>|t|) (p-value)"))
# Make a table for the data
kable(RNA_abundance,row.names=FALSE,caption="Table 1 Correlation of *norC* Abundance across Nitrogen Species and Oxygen Concentrations.")| Correlation | Estimate | Std. Error | t value | Pr(>|t|) (p-value) |
|---|---|---|---|---|
| DNA - N2O (uM) | -606.402 | 9418.328 | -0.064 | 0.951 |
| DNA - NH4 (uM) | 25.255 | 21.004 | 1.202 | 0.283 |
| DNA - NO2 (uM) | -2031.095 | 1383.87 | -1.468 | 0.202 |
| DNA - NO3 (uM) | -2.491 | 6.099 | -0.408 | 0.7 |
| DNA - O2 (uM) | -1.318 | 0.644 | -2.046 | 0.096 |
| RNA - N2O (uM) | -11946.518 | 9699.499 | -1.232 | 0.273 |
| RNA - NH4 (uM) | 56.903 | 11.746 | 4.844 | 0.005 |
| RNA - NO2 (uM) | -2268.704 | 1659.945 | -1.367 | 0.23 |
| RNA - NO3 (uM) | -11.349 | 5.228 | -2.171 | 0.082 |
| RNA - O2 (uM) | -1.27 | 0.855 | -1.485 | 0.198 |
NorC expression was found to significantly positively correlate with NH4+ levels (p = 0.005). No other significant correlations were observed at either the genome or the expression level.
Expression level significances (ranging p = 0.005-0.273) were generally higher than those of the genome level (ranging p = 0.202-0.951).
Discussion
While we only investigated a single component of the denitrification pathway, norC’s results can be related to the complex nitrogen metabolic network. At the shallow depth of 10m, norC gene copies and transcripts are not found at detectable levels (Figure 1), which suggests that the complete denitrification pathway is not active. There could be multiple reasons why the denitrification pathway is not active in surface waters. For one, there is ample oxygen and sunlight present for phototrophs to produce energy without requiring the less-efficient denitrification pathway [15, 16]. Second, nitrate is depleted at this depth (Figure 4) and any free nitrogen is likely required for primary production and assimilatory mechanisms to support amino acid and nucleotide synthesis [17].
Aerobic denitrification has been found in harsh environments where the oxygen concentration fluctuates. It is the main source of atmospheric N2O which has been rising due to the uncontrolled use of fertilizer in agriculture and the intensification of OMZs globally [18]. The trends in NO3−, NO2- and NH4+ levels across depths in Saanich Inlet (Figure 4) are consistent with literature in which heterotrophic microbes were found to utilize other oxidants, such as NO3- and NO2-, in anoxic environments [19]. This shift in metabolic activity from oxic to anoxic waters results in NO3- and NO2- being consumed and depleted in anoxic environments as well as NH4+ being produced. While denitrifying microbes produce NO and N2O, other heterotrophic microbes in anoxic environments reduce NO3- and NO2- to NH4+ , consistent with the observation of high NH4+ concentrations in the anoxic layer (Figure 4) [20]. The relationship between NH4+ levels and norC expression is shown to be positive and significant (Table 1).
Interestingly, the concentration of N2O peaks at 100m and gradually decreases, reaching a minimum at 200m (Figure 4). This could be seen as paradoxical since the abundance of norC gene copies and transcripts is increasing with depth and they are responsible for the production of N2O [20], so the concentration of N2O would be expected to increase with depth. However, N2O production is dependent on the presence of other genes involved in the denitrification pathway such as nar, nir, nor, and nis gene clusters [10]. Additionally, N2O production is also dependent on NO3- levels which follow a trend similar to N2O concentrations (Figure 4). Thus, it is tempting to argue that the highest N2O concentrations are found at ~100m because of the dependency of the denitrification pathway on NO3- as a substrate. However, NO3- should not be present where higher rates of denitrification are present since it is consumed by this process and denitrification rates have been shown to be greater at depths where oxygen is depleted [21]. Studies have also shown that N2O is consumed by microbes during the last step in canonical denitrification, which occurs in strictly anoxic environment [22]. This suggests that the the low N2O levels within the anoxic layer do not represent a low rate of N2O production, but instead the production of N2O and its subsequent consumption by the microbial community. This last step of the canonical denitrification pathway in which N2O is being consumed is done through the nitrous oxide reductase gene (nosZ) and therefore it would be interesting to investigate how active this gene is at depth in the inlet.
Many of the norC-containing species we identified are known to be important in the nitrogen cycle. Among the identified classes, Gammaproteobacteria contributed the most to norC DNA and RNA abundance across the majority of depths (Figure 3). This comes as no surprise because Gammaproteobacteria are known to be common marine denitrifiers [23, 24]. Now focusing our analysis at the family taxonomic level, we found that Candidatus Thioglobus contributed the most to norC DNA abundance (Figure 2), which is consistent with literature as the members of that family have been found to produce nitrate, while they are also abundant in OMZs [25]. Interestingly, the family Helicobacteraceae has been found to co-occur with denitrification on elemental sulfur particles [26], which our results support because we only observed high norC expression in Helicobacteraceae at a depth of 200m where sulphur is expected to be highest (Figure 2) [3]. Elucidating which clades contribute to the denitrification pathway by expressing norC is important because it completes one piece of the puzzle that is the nitrogen distributed metabolic network.
Distributed metabolism as demonstrated by the nitrogen cycle is beneficial for the overall survival of the pathway. For example, nitrogenase genes are spread out among different microbes in aerobic and anaerobic environments [27]. This broad distribution of nitrogen-fixing genes reflects the different environments these microbes inhabit [27]. Since the nitrogen cycle is vital for the survival of many plants and animals, a greater distribution of important genes and components for the cycle increases the likelihood of gene persistence through environmental adaptation. Without norC, the denitrification pathway would be incomplete, impacting the entire nitrogen cycle. Denitrification is important for converting nitrate to nitrogen gas by removing bioavailable nitrogen and returning it to the atmosphere [27]. Without this step, nitrogen would remain fixed as the nitrate state in the ecosystem [27]. In terrestrial ecosystems, adding nitrogen can cause nutrient imbalance in trees and change the forest health, eventually leading to a decline in biodiversity [27]. With recent developments in agriculture, much of the nitrogen used in urban and agricultural activities eventually leaches out of the soil and into bodies of water like rivers and streams [27]. In nearshore marine systems, excess nitrogen can lead to eutrophication [27]. To avoid such catastrophic changes to the environment, having vital nitrogen genes distributed among different microbial species prevents accumulation of nitrogen in the anoxic zones of lakes and oceans. Species that cannot adapt to extreme environmental pressures, whether occurring naturally or through human industrial alterations, will not cause the breakdown of the nitrogen cycle. Other microbes that have the capacity to survive harsh environments and have the corresponding genes, will take their place within the denitrification step, ensuring a seamless continuation of the major transformations within the cycle.
When we consider the evolutionary benefits for having a distributed metabolism among different microbes, one potential benefit may be that by doing so, microbes can make their genomes smaller. Having a smaller genome can provide a selective advantage. Giovannoni studied how B vitamins are distributed in complex patterns [28]. An important point that he brings up is that survival of the different plankton species all depends on vitamins that are made within the community - “a cell that needs a vitamin can only thrive in a community that provides it” [28]. Also, connectedness has become an important concept because it can help explain the response of biological communities to stresses [28]. Giovannoni observes that in fluctuating ocean conditions, the fluxes of reduced sulfur and glycine are sufficient to meet a certain species’ requirement most of the time [28]. By relying on the natural sulfur and glycine, the microbes no longer need the genes to reduce the sulfur or glycine themselves, thereby reducing the energy expended by the microbes. This suggests that the fitness advantages of smaller genomes and lower cellular costs offset the potential gain in fitness that would come from producing the required compounds when they are in short supply in the environment [28]. This is not a direct correlation of the nitrogen cycle, but a concept can be extracted from this example. By distributing genes for the nitrogen cycle, with smaller genomes microbes have a selective advantage for survival.
Future studies should examine the potential presence of metabolic interactions between genera expressing norC gene. These possible interactions may impact and alter the abundance and the role of these genera within the community at a specific depth. Although our study focused on one specific gene, the methods could be used to examine the abundance of other genes to further gain insight regarding other biogeochemical processes through microbial metabolism. So one could further examine the genomes of the organisms present in the sample to determine whether there are any other distributed metabolisms among the species. Perhaps other pathways would be discovered, where the bacteria are interdependent on one another, each providing substrates for the other.
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Appendix
Figure A1 FPKM RNA abundance (coloured bars from purple to red) of norC expression across sample depth with respect to taxonomic assignments. Selected clades are numbered (1: Gammaproteobacteria; 2: Epsilonproteobacteria; 3: Ardenticatenia; 4: Clostridia; 5: Negativicutes). FPKM bar heights are relative within the same colour only. NorC expression is not observed in species outside of what is shown.